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 08/20/2024 is in compliance
with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-20 are rejected under U.S.C 101 for containing an abstract idea without significantly more.
Regarding claim 1:
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is a process.
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim recites an abstract idea.
generating, [by one or more processors and using a machine learning model], a predictive activity sequence for a user that comprises a plurality of activity predictions corresponding to a plurality of activity time segments within an evaluation time period; - This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed in the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) Ill. C.)
generating, [by the one or more processors] and based on the predictive activity sequence, a personalized activity sequence for the user that comprises a plurality of activity subgoals corresponding to one or more activity time segments of the plurality of activity time segments within the evaluation time period; - This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed in the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) Ill. C.)
identifying, [by the one or more processors], an occurrence of an activity time segment corresponding to an activity subgoal of the plurality of activity subgoals; and - This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed in the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) Ill. C.)
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, there are no additional elements that integrate the judicial exception into a practical application. The additional elements:
by the one or more processor – This limitation is directed to a computer merely used as a tool to perform an existing process (see MPEP 2106.05(f) (2)).
in response to the occurrence of the activity time segment, providing, by the one or more processors, data indicative of the activity subgoal. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea [see MPEP 2106.05(f)] and therefore fails to integrate the exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, there are no additional elements that amount to significantly more than the judicial exception. The additional elements are:
by the one or more processor – This limitation is directed to a computer merely used as a tool to perform an existing process (see MPEP 2106.05(f) (2)).
in response to the occurrence of the activity time segment, providing, by the one or more processors, data indicative of the activity subgoal. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea [see MPEP 2106.05(f)] and therefore fails to integrate the exception into a practical application.
Regarding claim 2,
Claim 2 is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 1 which includes an abstract idea (see rejection for claim 1). The additional limitations:
wherein the machine learning model comprises an encoder-decoder network previously trained using a plurality of historical activity sequences for a cohort of users. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea [see MPEP 2106.05(f)] and therefore fails to integrate the exception into a practical application.
Regarding claim 3,
Claim 3 is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 2 which includes an abstract idea (see rejection for claim 2). The additional limitations:
wherein the machine learning model is fine-tuned using one or more user-specific historical activity sequences of the plurality of historical activity sequences that correspond to the user. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea [see MPEP 2106.05(f)] and therefore fails to integrate the exception into a practical application.
Regarding claim 4,
Claim 4 is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 3 which includes an abstract idea (see rejection for claim 3). The additional limitations:
receiving an activity sequence for the evaluation time period; and Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea [see MPEP 2106.05(f)] and therefore fails to integrate the exception into a practical application.
in response to the activity sequence, updating one or more parameters of the machine learning model. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea [see MPEP 2106.05(f)] and therefore fails to integrate the exception into a practical application.
Regarding claim 5,
Claim 5 is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 1 which includes an abstract idea (see rejection for claim 1). The additional limitations:
wherein the activity subgoal is generated based on an activity prediction of the plurality of activity predictions that corresponds to the activity time segment. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea [see MPEP 2106.05(f)] and therefore fails to integrate the exception into a practical application
Regarding claim 6,
Claim 6 is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 5 which includes an abstract idea (see rejection for claim 5). The additional limitations:
wherein the personalized activity sequence comprises the predictive activity sequence that is augmented with one or more reward values and a reward value of the one or more reward values indicates a degree to which the activity subgoal exceeds the activity prediction. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea [see MPEP 2106.05(f)] and therefore fails to integrate the exception into a practical application
Regarding claim 7,
Claim 7 is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 5 which includes an abstract idea (see rejection for claim 5). The additional limitations:
wherein the activity subgoal is selected from the plurality of activity predictions. - This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed in the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) Ill. C.)
Regarding claim 8,
Claim 8 is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 1 which includes an abstract idea (see rejection for claim 1). The additional limitations:
wherein the predictive activity sequence is based on one or more contextual factors corresponding to the user. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea [see MPEP 2106.05(f)] and therefore fails to integrate the exception into a practical application
Regarding claim 9,
Claim 9 is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 1 which includes an abstract idea (see rejection for claim 1). The additional limitations:
wherein a number of the plurality of activity time segments is based on a type of activity associated with the personalized activity sequence. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea [see MPEP 2106.05(f)] and therefore fails to integrate the exception into a practical application
Regarding claim 10,
Claim 10 is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 1 which includes an abstract idea (see rejection for claim 1). The additional limitations:
wherein the data indicative of the activity subgoal is provided prior to the activity time segment. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea [see MPEP 2106.05(f)] and therefore fails to integrate the exception into a practical application
Regarding claim 11, it is rejected under the same rationale as independent claim 1 as they are analogous claims.
Regarding claim 12, it is rejected under the same rationale as dependent claim 2 as they are analogous claims.
Regarding claim 13, it is rejected under the same rationale as dependent claim 3 as they are analogous claims.
Regarding claim 14, it is rejected under the same rationale as dependent claim 4 as they are analogous claims.
Regarding claim 15, it is rejected under the same rationale as dependent claim 5 as they are analogous claims.
Regarding claim 16, it is rejected under the same rationale as dependent claim 6 as they are analogous claims.
Regarding claim 17, it is rejected under the same rationale as dependent claim 7 as they are analogous claims.
Regarding claim 18, it is rejected under the same rationale as dependent claim 8 as they are analogous claims.
Regarding claim 19, it is rejected under the same rationale as dependent claim 9 as they are analogous claims.
Regarding claim 20, it is rejected under the same rationale as independent claim 1 as they are analogous claims.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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)(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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-5, 8-15 and 18-20 are rejected under 35 U.S.C. 102 (a) (1) as being anticipated by Baykaner et al. (US 2020/0170549 A1).
Regarding claim 1, Baykaner explicitly teaches:
generating, by one or more processors and using a machine learning model, a predictive activity sequence for a user that comprises a plurality of activity predictions corresponding to a plurality of activity time segments within an evaluation time period; (Baykaner, ¶[0018]: “the model may learn that at 2pm the user generally does 1 hour of intense exercise, and this identified activity feature, of an hour's intensive exercise at 2pm, can be incorporated into the prediction (the adjusted user daily activity profile) for the following day. So, identifying the one or more activity features may be based on all the conditioning variables fed in to the model ( e.g. the demographics, the timing, activity intensity, and the control variable if used) in whatever organisation the model learns to best utilise these features (which may include totally ignoring irrelevant features).”)
generating, by the one or more processors and based on the predictive activity sequence, a personalized activity sequence for the user that comprises a plurality of activity subgoals corresponding to one or more activity time segments of the plurality of activity time segments within the evaluation time period; (Baykaner, ¶[0143]: “If the particular activity level of a user for the particular day is lower than daily target activity level, the apparatus may be configured to decrease the daily target activity level for the subsequent day. For example, if the user has not been studying at all and decided to aim to study for two hours a day, then is only managing one and a half hours a day, the daily target activity level may be adjusted from two hours to one and a half hours to give the user a more manageable goal. The one-and-a-half-hour goal, once met, may then be incrementally increased over a predetermined period of several days ( e.g. a week) to the original two hours target for study activity.”)
identifying, by the one or more processors, an occurrence of an activity time segment corresponding to an activity subgoal of the plurality of activity subgoals; and (Baykaner, ¶[0018]: “In some examples, to identify activity features as candidates for adjustment, the model may not explicitly classify the activity features. For example, the model discussed with reference to FIG. 4 does not explicitly classify activity features (e.g. as a "swimming" peak, a 'jogging" peak, etc.), but it does identify the correlations between the time conditioning information and the raw activity data. For example, the model may learn that at 2pm the user generally does 1 hour of intense exercise, and this identified activity feature, of an hour's intensive exercise at 2pm, can be incorporated into the prediction (the adjusted user daily activity profile) for the following day.”)
in response to the occurrence of the activity time segment, providing, by the one or more processors, data indicative of the activity subgoal. (Baykaner, ¶[0018]: “In some examples, to identify activity features as candidates for adjustment, the model may not explicitly classify the activity features. For example, the model discussed with reference to FIG. 4 does not explicitly classify activity features (e.g. as a "swimming" peak, a 'jogging" peak, etc.), but it does identify the correlations between the time conditioning information and the raw activity data. For example, the model may learn that at 2pm the user generally does 1 hour of intense exercise, and this identified activity feature, of an hour's intensive exercise at 2pm, can be incorporated into the prediction (the adjusted user daily activity profile) for the following day.”)
Regarding claim 2, Baykaner teaches all the limitations in claim 1 (as shown in the rejections above).
Baykaner further teaches:
wherein the machine learning model comprises an encoder-decoder network previously trained using a plurality of historical activity sequences for a cohort of users. (Baykaner, ¶[0107]: “Using a machine learning technique, for example a deep learning and generative method such as a Variational Autoencoder (VAE) or Deep Conditional Generative Adversarial Network (DCGAN), it is possible to produce predictions about the activity of a user for the following day in an output predicted activity 412. These predictions may be based on an input 402 containing the activity information of the user from the previous day, as well as other conditioning data (e.g. the time of day 414, and/or demographic and psychometric data about the user 416).”)
Regarding claim 3, Baykaner teaches all the limitations in claim 2 (as shown in the rejections above).
Baykaner further teaches:
wherein the machine learning model is fine-tuned using one or more user-specific historical activity sequences of the plurality of historical activity sequences that correspond to the user. (Baykaner, ¶[0012]: “In some examples, the model, once trained, may be used to generate an adjusted user daily activity profile following input of a user daily input profile (e.g. the previous day's activity logged for the user).”)
Regarding claim 4, Baykaner teaches all the limitations in claim 3 (as shown in the rejections above).
Baykaner further teaches:
receiving an activity sequence for the evaluation time period; and (Bayknaer, ¶[0131]: “As plotted in FIG. 6, the activity level 604 may also indicate the differential of, for example, calories used, steps taken, distance travelled, completion status of a task, or time asleep, which are all parameters which accumulate over the period of a day. Of course, the activity level for a user may alternatively be plotted as a cumulative step-type plot of calories used, steps taken, distance travelled, completion status of a task, or time asleep in some examples.”, ¶[0132]: “For example, as shown in FIG. 6, the user may have gone for a morning jog 606 around 8am as a medium intensity activity, then rested until lunchtime when they went for a low intensity walk 608. At around 19:00 the user went to the gym and did a high intensity ( e.g. cardio) exercise session followed by a lower intensity ( e.g. swimming) activity 610. FIGS. 7a-d illustrate the effect of setting a control value (a received daily target activity level for the user) 418 of"6" (FIGS. 7a and 7b), and "7" (FIGS. 7c and 7 d), compared with the lower particular activity level for the day (for example, the lower particular activity level for the day was "5").”)
in response to the activity sequence, updating one or more parameters of the machine learning model. (Baykaner, ¶[0055-0066]: “The user daily activity profile and the adjusted user daily activity profile may provide an indication of the variation of one or more of the following parameters with time: calories used, heart rate, steps taken, distance travelled, body temperature, body hydration and perspiration, breathing rate, blood oxygenation, blood-sugar level, completion status of a task, and time asleep.”, ¶[0070]: “The apparatus may be configured to measure the one or more parameters indicated in the adjusted user daily activity profile ( e.g. the apparatus may comprise a heart rate monitor, a pedometer or a thermal sensor).”)
Regarding claim 5, Baykaner teaches all the limitations in claim 1 (as shown in the rejections above).
Baykaner further teaches:
wherein the activity subgoal is generated based on an activity prediction of the plurality of activity predictions that corresponds to the activity time segment. (Baykaner, ¶[0143]: “If the particular activity level of a user for the particular day is lower than daily target activity level, the apparatus may be configured to decrease the daily target activity level for the subsequent day. For example, if the user has not been studying at all and decided to aim to study for two hours a day, then is only managing one and a half hours a day, the daily target activity level may be adjusted from two hours to one and a half hours to give the user a more manageable goal. The one-and-a-half-hour goal, once met, may then be incrementally increased over a predetermined period of several days ( e.g. a week) to the original two hours target for study activity.”)
Regarding claim 8, Baykaner teaches all the limitations in claim 1 (as shown in the rejections above).
Baykaner further teaches:
wherein the predictive activity sequence is based on one or more contextual factors corresponding to the user. (Baykaner, ¶[0020]: “Another example would be to use an activity classifier (e.g. another machine learning model) to generate activity type predictions. Such a model could take, for example, accelerometry, timing, wifi/radar, heart-rate and/or other data as input, and output the time and classes of activities (e.g. swimming from 2-3pm). These output classifications could then be fed into the activity generator model just as if they were annotations from a user.”)
Regarding claim 9, Baykaner teaches all the limitations in claim 1 (as shown in the rejections above).
Baykaner further teaches:
wherein a number of the plurality of activity time segments is based on a type of activity associated with the personalized activity sequence. (Baykaner, ¶[0020]: “Another example would be to use an activity classifier (e.g. another machine learning model) to generate activity type predictions. Such a model could take, for example, accelerometry, timing, wifi/radar, heart-rate and/or other data as input, and output the time and classes of activities (e.g. swimming from 2-3pm). These output classifications could then be fed into the activity generator model just as if they were annotations from a user.”)
Regarding claim 10, Baykaner teaches all the limitations in claim 1 (as shown in the rejections above).
Baykaner further teaches:
wherein the data indicative of the activity subgoal is provided prior to the activity time segment. (Baykaner, ¶0138]: “By showing the user their predicted activity 708, 710 in advance (e.g. at the start of the day) and then challenging the user to meet ( or exceed) it, the user may be encouraged to be more active than they would otherwise have been, and can check in real-time how they are faring against their prediction.”)
Regarding independent claim 11 and 20, it is rejected under the same rationale with independent claim 1 as they are analogous claims.
Regarding dependent claim 12, it is rejected under the same rationale with dependent claim 2 as they are analogous claims.
Regarding dependent claim 13, it is rejected under the same rationale with dependent claim 3 as they are analogous claims.
Regarding dependent claim 14, it is rejected under the same rationale with dependent claim 4 as they are analogous claims.
Regarding dependent claim 15, it is rejected under the same rationale with dependent claim 5 as they are analogous claims.
Regarding dependent claim 18, it is rejected under the same rationale with dependent claim 8 as they are analogous claims.
Regarding dependent claim 19, it is rejected under the same rationale with dependent claim 9 as they are analogous claims.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 6-7 and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Baykaner et al. (US 2020/0170549 A1) in view of Chang et al. (US 11,228,613 B2).
Regarding claim 6, Baykaner explicitly teaches all the limitations in claim 5 (as shown in the rejections above).
Baykaner fails to teach:
wherein the personalized activity sequence comprises the predictive activity sequence that is augmented with one or more reward values and a reward value of the one or more reward values indicates a degree to which the activity subgoal exceeds the activity prediction.
However, Chang(2) explicitly teaches:
wherein the personalized activity sequence comprises the predictive activity sequence that is augmented with one or more reward values and a reward value of the one or more reward values indicates a degree to which the activity subgoal exceeds the activity prediction. (Chang(2), Col. 15, Lines 59-66: “Because the goals are to maximize the beneficial final outcome or minimize the negative final outcome, the total reward can be defined as the final outcome of interest Yi if it is beneficial, or as – Yi if it is hazardous. A parametric regression model for the Q function can be used at each stage to learn the optimal policy that maximizes the reward for a finite time horizon (i.e., the final stage T, is a finite 65 number)” ,Col. 16, Lines 26-35: “To formulate the problem in mathematical terms, Rit is defined to be the dichotomized health outcome of the participant i in the stage t, where Rit= ( 0it >C,), C, is the threshold to be estimated at each stage, and I( ) is an indicator function. Therefore, if the outcome, 0it exceeds Ct, then Rit = 1. Otherwise, Rit = O. The threshold, Ct can be considered to be the outcome goal option (e.g., stress level). Therefore, Rit indicates whether the participant i has met the goal in the stage t. ”)
The combination of Baykaner and Chang are analogous art because they are in the same field of training time series data. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention, having the teachings of Baykaner and Chang before them, to modify the teachings of Baykaner to include the teachings of Chang to improve policy accuracy while reducing data entry workload and network traffic.
Regarding claim 7, Baykaner explicitly teaches all the limitations in claim 1 (as shown in the rejections above).
Baykaner fails to teach:
wherein the activity subgoal is selected from the plurality of activity predictions.
However, Chang explicitly teaches:
wherein the activity subgoal is selected from the plurality of activity predictions. (Chang, Col. 19, Lines 43-47: “A dialog system can also provide a meaningful interpretation of the policy and sub-goals to explain, for instance, why actions have been selected, why particular sub-goals have been selected, and impacts of adjustments to the sub-goals and actions.”)
Regarding dependent claim 16, it is rejected under the same rationale with dependent claim 6 as they are analogous claims.
Regarding dependent claim 17, it is rejected under the same rationale with dependent claim 7 as they are analogous claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AMY TRAN whose telephone number is (571)270-0693. The examiner can normally be reached Monday - Friday 7:30 am - 5:00 pm 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, David Yi can be reached at (571) 270-7519. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/AMY TRAN/Examiner, Art Unit 2126
/DAVID YI/Supervisory Patent Examiner, Art Unit 2126
Prosecution Insights