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 .
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 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite a mental process of observation, evaluation and judgement . This judicial exception is not integrated into a practical application nor does it amount to significantly more because the additional elements that are merely insignificant extra-solution activity in combination with generic computer hardware used to implement the abstract idea, see the analysis below for further details.
Claims 1, 19 and 20
Step 1: The claim recites a method, apparatus and non-transitory computer-readable storage medium, therefore, they falls into the statutory categories.
Step 2A Prong 1: The claim recites, inter alia:
Generating vectorized low frequency data by converting the low-frequency data; (This is a mental process of observation, evaluation and judgment wherein a user creates a vector for low frequency data such as clinical notes or other data recorded on sporadic basis. Can be done with the aid of pen and paper.)
Step 2A Prong 2:
This judicial exception is no integrated into a practical application. Aside from the limitations above, the claim recites:
Receiving high-frequency data associated with a first capture rate and low-frequency data associated with a second capture rate; (This is extra-solution activity of data collection, see MPEP 2106.05(g).)
using a low-frequency encoding model, one or more processors, using non-transitory computer-readable storage medium including instruction; (This amounts to using generic computer hardware to execute the abstract idea cited above, see MPEP 2106.05(f).)
process, utilizing a prediction model, the high-frequency data and the vectorized low-frequency data to generate output data. (This is cited a high level of generality and results in 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) – Examiner’s note: high level recitation of using a machine learning model);
at least one process and at least one memory including program code, the at lest one memory and the at least one program code; (Claim 19) a computer program product comprising a non-transitory computer readable storage medium, the non-transitory computer-readable storage medium including instruction that, when executed by the at least one processor (Claim 20); (These additional elements amount to use generic computer hardware to execute the abstract idea, see MPEP 2106.05(f).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity in combination of generic computer hardware performing generic functions that are implemented to perform the disclosed abstract idea above.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of “Receiving high-frequency data associated with a first capture rate and low-frequency data associated with a second capture rate;” is well-understood, routine and conventional, see MPEP 2106.05(d)(II)(i) wherein it cites “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016)”.
The additional element of “using a low-frequency encoding model, one or more processors, using non-transitory computer-readable storage medium including instruction; at least one process and at least one memory including program code, the at least one memory and the at least one program code; (Claim 19) a computer program product comprising a non-transitory computer readable storage medium, the non-transitory computer-readable storage medium including instruction that, when executed by the at least one processor (Claim 20);” amounts to using generic computer hardware to execute the abstract idea cited above, see MPEP 2106.05(f). Lastly the additional element of “process, utilizing a prediction model, the high-frequency data and the vectorized low-frequency data to generate output data.” is cited a high level of generality and results in 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).
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
Claim 2
Step 2A Prong 1: The claim recites, inter alia:
Selecting an optimal variant from the plurality of variants of the prediction model based at least in part on performance data corresponding to the ach variant of the plurality of variants, wherein the prediction model utilized to generate the output data comprises the optimal variant. (This is a mental process of observation, judgment and evaluation wherein a user picks the best performing model based performance data.)
Step 2A Prong 2:
This judicial exception is no integrated into a practical application. Aside from the limitations above, the claim recites:
Receiving output truth source data; (This is data collection and extra-solution activity, see MPEP 2106.05(g).)
Generating a plurality of variants of the prediction model, wherein each variant predicts the output truth source data based at least in part on the high-frequency data by at least: (1) introducing the vectorized low frequency data in combination with the high-frequency data at a different point in the training for each variant of the plurality of variants of the prediction model, and (2) completing the training upon introduction of the vectorized low frequency data; and (This is cited a high level of generality and results in 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) – Examiner’s note: high level recitation of using a variety of machine learning models trained by using data in different ways to process received data to get output or prediction.);
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere generic computer hardware performing generic functions that are implemented to perform the disclosed abstract idea above.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional limitations are:
Receiving output truth source data; (This is data collection and extra-solution activity, see MPEP 2106.05, which is well-understood, routine and conventional. See MPEP 2106.05(d)(II)(i) wherein it cites “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016)”.
Generating a plurality of variants of the prediction model, wherein each variant predicts the output truth source data based at least in part on the high-frequency data by at least: (1) introducing the vectorized low frequency data in combination with the high-frequency data at a different point in the training for each variant of the plurality of variants of the prediction model, and (2) completing the training upon introduction of the vectorized low frequency data; and (This is cited a high level of generality and results in 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) – Examiner’s note: high level recitation of using a variety of machine learning models trained by using data in different ways to process received data to get output or prediction.);
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are extra-solution activity in combination with generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
Claim 3
Step 2A Prong 1: The claim recites, inter alia:
Generating at least on state prediction based on high-frequency and low frequency data. (This is a mental process of observation, evaluation and judgment wherein a user considers all the data and makes a prediction or determination. For example, a doctor could look at a patient’s blood pressure readings or pulse readings (high frequency data) and the user weight or health history (low frequency data) and make a prediction or determination on the user health.)
Step 2A Prong 2:
This judicial exception is no integrated into a practical application. Aside from the limitations above, the claim recites:
processing, utilizing the prediction model. (This is cited a high level of generality and results in 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) – Examiner’s note: high level recitation of using a machine learning model to process received data to get output or prediction.);
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere generic computer hardware performing generic functions that are implemented to perform the disclosed abstract idea above.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
processing, utilizing the prediction model. (This is cited a high level of generality and results in 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) – Examiner’s note: high level recitation of using a machine learning model to process received data to get output or prediction.);
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
Claim 4
Step 2A Prong 1: The claim recites, inter alia:
Generating at least one missing data value associated with the high-frequency data prediction based on high-frequency and low frequency data. (This is a mental process of observation, evaluation and judgment wherein a user considers all the data and makes a prediction or determination. For example, a doctor could look at a patient’s blood pressure readings or pulse readings (high frequency data) and the user weight or health history (low frequency data) and make a prediction or determination on the user health such as the user’s blood pressure will continue to rise. This is a missing data value as the instant specification in para. [0163] cites “Additionally or alternatively, in some embodiments, the missing data value includes a predicted data value for a subsequent or future timestamp, for example the next data value predicted in a time series.” .)
Step 2A Prong 2:
This judicial exception is no integrated into a practical application. Aside from the limitations above, the claim recites:
processing, utilizing the prediction model. (This is cited a high level of generality and results in 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) – Examiner’s note: high level recitation of using a machine learning model to process received data to get output or prediction.);
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere generic computer hardware performing generic functions that are implemented to perform the disclosed abstract idea above.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
processing, utilizing the prediction model. (This is cited a high level of generality and results in 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) – Examiner’s note: high level recitation of using a machine learning model to process received data to get output or prediction.);
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
Claim 5
Step 2A Prong 1: The claim recites, inter alia:
Inherits the abstract idea of claim 1.
Step 2A Prong 2:
This judicial exception is no integrated into a practical application. Aside from the limitations above, the claim recites:
wherein the prediction model comprises a modified U-net architecture model. (This is cited a high level of generality and results in 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) – Examiner’s note: high level recitation of using a machine learning model being a modified u-net model.)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into a practical application as they are mere generic computer hardware performing generic functions that are implemented to perform the disclosed abstract idea above.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional limitations are:
“wherein the prediction model comprises a modified U-net architecture model”, which is cited a high level of generality and results in 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).
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
Claim 6
Step 2A Prong 1: The claim recites, inter alia:
Inherits the abstract idea of claim 1.
Step 2A Prong 2:
This judicial exception is no integrated into a practical application. Aside from the limitations above, the claim recites:
wherein receiving the high-frequency data comprises capturing the high-frequency data utilizing at least one sensor. (This step of capturing the data is data collection and thus extra-solution activity, see MPEP 2106.05(g). The use of a at least one sensor is using generic computer hardware to execute the abstract idea, see MPEP 2106.05(f).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into a practical application as they are mere extra-solution activity in combination with generic computer hardware performing generic functions that are implemented to perform the disclosed abstract idea above.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional limitations are:
wherein receiving the high-frequency data comprises capturing the high-frequency data utilizing at least one sensor.” is well-understood, routine and conventional. See MPEP 2106.06(d)(II)(i) wherein it cites “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016)”. The use of the sensor is using generic computer hardware performing generic computer functions to execute the abstract idea, see MPEP 2106.05(f).
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are extra-solution activity in combination with generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
Claim 7
Step 2A Prong 1: The claim recites, inter alia:
Inherits the abstract idea of claim 2.
Step 2A Prong 2:
This judicial exception is no integrated into a practical application. Aside from the limitations above, the claim recites:
wherein receiving the output truth source data comprises receiving user input indicating at least one state associated with at least one portion of the low-frequency data and at least one portion of the high-frequency data. (This step of capturing the data is data collection and thus extra-solution activity, see MPEP 2106.05(g).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into a practical application as they are mere extra-solution activity in combination with the disclosed abstract idea above.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional limitations are:
“wherein receiving the output truth source data comprises receiving user input indicating at least one state associated with at least one portion of the low-frequency data and at least one portion of the high-frequency data.” is well-understood, routine and conventional. See MPEP 2106.06(d)(II)(i) wherein it cites “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016)”.
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are extra-solution activity in combination with the disclosed abstract idea above.
Claim 8
Step 2A Prong 1: The claim recites, inter alia:
deriving the output truth source data based at least in part on the historical high-frequency data. (This is a mental process of observation, evaluation and judgement wherein a user makes a decision of prediction based on historical data.)
Step 2A Prong 2:
This judicial exception is no integrated into a practical application. Aside from the limitations above, the claim recites:
Receiving historical high-frequency data; and (This step of capturing the data is data collection and thus extra-solution activity, see MPEP 2106.05(g).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into a practical application as they are mere extra-solution activity in combination with the disclosed abstract idea above.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional limitations are:
“Receiving historical high-frequency data;” is well-understood, routine and conventional. See MPEP 2106.06(d)(II)(i) wherein it cites “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016)”.
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are extra-solution activity in combination with the disclosed abstract idea above.
Claim 9
Step 2A Prong 1: The claim recites, inter alia:
identifying a unique code set from the low-frequency data; (This is mental process of user identifying codes in data.)
generating a code data vector set by at least converting each unique code in the unique code set to a code data vector; (This is mental process of a user creating a vector for each code, cand be done with aid of pen and paper.)
generating a set of time-by-code vectors by at least, for each instance of the unique code in the low-frequency data:
determining a time differential between a recordation timestamp associated with the instance of the unique code and a sensed timestamp associated with at least a portion of the high-frequency data; (This is mental of process of observation, judgment and evaluation wherein a user compares the timestamp of a code to a nearest timestamp of high frequency data and determines a difference between the two times.)
generating a transformed time vector based at least in part on the time differential, wherein the transformed time vector is generated utilizing a time transformation function; (This is mental process of observation, evaluation and judgment wherein a user performs a mathematical operation using the time difference. This is also supported by instant specification in para. [0051] wherein it cites “Time transformation function” may refer to a mathematical algorithm or function…” and para. [0181] which cites “In some embodiments, the transformed time vector is generated utilizing a time transformation function. For example, in some embodiments, the apparatus 400 applies the time differential to the time transformation function to generate the transformed time vector.”, thus it is inputting the time differential into a mathematical formula or function and getting a vector. Can be done with e aid of pen and paper.)
concatenate the transformed time vector associated with the instance of the unique code with a code data vector from the code data vector set, wherein the code data vector corresponds to the instance of the unique code; and (This is mental process of combining two vectors, the transformed time vector and the code data vector, can be done with aid of pen and paper.)
Step 2A Prong 2:
This judicial exception is no integrated into a practical application. Aside from the limitations above, the claim recites:
generating a combined vector by at least applying the set of time-by-code vectors to an attention model that generates the combined vector, wherein the combined vector comprises the vectorized low frequency data. (This is cited a high level of generality and results in 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) – Examiner’s note: high level recitation of inputting a set of vectors to an attention model (machine learning model) that generates output (combined vectors/vectorized low frequency data). );
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere generic computer hardware performing generic functions that are implemented to perform the disclosed abstract idea above.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional limitations are:
“generating a combined vector by at least applying the set of time-by-code vectors to an attention model that generates the combined vector, wherein the combined vector comprises the vectorized low frequency data.” Which is cited a high level of generality and results in 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). Examiner’s note: high level recitation of inputting a set of vectors to an attention model (machine learning model) that generates output (combined vectors/vectorized low frequency data).
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
Claim 10
Step 2A Prong 1: The claim recites, inter alia:
Claim 10 inherits the abstract idea of claim 9.
Step 2A Prong 2:
This judicial exception is no integrated into a practical application. Aside from the limitations above, the claim recites:
wherein the combined vector comprises a weighted average of each time-by-code vector in the set of time-by-code vectors, wherein the weighted average is determined based at least in part on a set of weights generated utilizing the attention model comprising a multi-head attention layer. (This is cited a high level of generality and results in 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).);
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere generic computer hardware performing generic functions that are implemented to perform the disclosed abstract idea above.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional limitations are:
wherein the combined vector comprises a weighted average of each time-by-code vector in the set of time-by-code vectors, wherein the weighted average is determined based at least in part on a set of weights generated utilizing the attention model comprising a multi-head attention layer. (This is cited a high level of generality and results in 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).);
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
Claim 11
Step 2A Prong 1: The claim recites, inter alia:
Claim 11 inherits the abstract idea of claim 9.
Step 2A Prong 2:
This judicial exception is no integrated into a practical application. Aside from the limitations above, the claim recites:
wherein the multi-head attention layer is trained to learn a relevance of the unique code to a state prediction, and an importance of the time differential corresponding to the unique code. (This is cited a high level of generality and results in 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).) – Examiner’s note: high-level recitation of a machine learning model being trained.)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere generic computer hardware performing generic functions that are implemented to perform the disclosed abstract idea above.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional limitations are:
wherein the multi-head attention layer is trained to learn a relevance of the unique code to a state prediction, and an importance of the time differential corresponding to the unique code. (This is cited a high level of generality and results in 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).) – Examiner’s note: high-level recitation of a machine learning model being trained.)
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
Claim 12
Step 2A Prong 1: The claim recites, inter alia:
Claim 12 inherits the abstract idea of claim 9.
Step 2A Prong 2:
This judicial exception is no integrated into a practical application. Aside from the limitations above, the claim recites:
wherein the attention model is configured based at least in part on a user-specific vector. (This is cited a high level of generality and results in 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).) – Examiner’s note: high-level recitation of a machine learning model using user-specific vector.)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere generic computer hardware performing generic functions that are implemented to perform the disclosed abstract idea above.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional limitations are:
wherein the attention model is configured based at least in part on a user-specific vector. (This is cited a high level of generality and results in 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).) – Examiner’s note: high-level recitation of a machine learning model using user-specific vector.)
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
Claim 13
Step 2A Prong 1: The claim recites, inter alia:
Claim 13 inherits the abstract idea of claim 9.
Step 2A Prong 2:
This judicial exception is no integrated into a practical application. Aside from the limitations above, the claim recites:
wherein the user-specific vector comprises patient demographic data. (This is linking the abstract idea to particular technology field, see MPEP 2106.05(h).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as merely linking the abstract idea to a particular technological field.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional limitations are:
wherein the user-specific vector comprises patient demographic data. (This is linking the abstract idea to particular technology field, see MPEP 2106.05(h).)
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as merely links the abstract idea to a particular technological field.
Claim 14
Step 2A Prong 1: The claim recites, inter alia:
Generating the user-specific vector based at least in part on historical data. (This is mental process of observation, evaluation and judgment wherein a user creates a patient vector based patient historical data, can be done with aid of pen and paper.)
Step 2A Prong 2:
This judicial exception is no integrated into a practical application. Aside from the limitations above, there are not additional limitations.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. There are no additional limitations.
Claim 15
Step 2A Prong 1: The claim recites, inter alia:
Claim 15 inherits the abstract ideas in claim 9.
Step 2A Prong 2:
This judicial exception is no integrated into a practical application. Aside from the limitations above, the additional limitations are:
wherein converting each unique code in the unique code set to the code data vector comprises: for each unique code: applying the unique code to a trained language model, wherein the trained language model generates the code data vector corresponding to the unique code. (This is cited a high level of generality and results in 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) – Examiner’s note: high level recitation of using a machine learning model by inputting unique codes and getting output of code data vector.);
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere generic computer hardware performing generic functions that are implemented to perform the disclosed abstract idea above.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional limitations are:
wherein converting each unique code in the unique code set to the code data vector comprises: for each unique code: applying the unique code to a trained language model, wherein the trained language model generates the code data vector corresponding to the unique code. (This is cited a high level of generality and results in 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) – Examiner’s note: high level recitation of using a machine learning model by inputting unique codes and getting output of code data vector.);
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception are mere generic computer hardware performing generic functions that are implemented to perform the disclosed abstract idea above.
Claim 16
Step 2A Prong 1: The claim recites, inter alia:
wherein determining the time differential between the recordation timestamp associated with the instance of the unique code and the sensed timestamp associated with at least the portion of the high-frequency data comprises: determining the sensed timestamp associated with at least the portion of the high-frequency data, wherein the sensed timestamp comprises one of a first sensed timestamp, a last sensed timestamp, or a predetermined timestamp associated with at least the portion of the high-frequency data. (This is mental process of observation, evaluation and judgment wherein a user determines a timestamp associated with data.)
Step 2A Prong 2:
This judicial exception is no integrated into a practical application. Aside from the limitations above, there are not additional limitations.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. There are no additional limitations.
Claim 17
Step 2A Prong 1: The claim recites, inter alia:
Generating a scaled time differential based at least in part of the time differential, wherein the transformed time vector is generated by applying the scaled time differential to the time transformation function. (This is mental process of observation, evaluation and judgment wherein a user performs a mathematical operation using the time difference. The first scaled time different is inputting the time differential into a logarithmic scaling function as cited in claim 18. Then the transformed vector portion is input the scaled time differential into a time function. Thus, it a user solving two mathematical functions and can be done with aid of pen and paper. This is also supported by instant specification in para. [0051] wherein it cites “Time transformation function” may refer to a mathematical algorithm or function…” and para. [0181] which cites “In some embodiments, the transformed time vector is generated utilizing a time transformation function. For example, in some embodiments, the apparatus 400 applies the time differential to the time transformation function to generate the transformed time vector.”)
Step 2A Prong 2:
This judicial exception is no integrated into a practical application. Aside from the limitations above, there are not additional limitations.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. There are no additional limitations.
Claim 18
Step 2A Prong 1: The claim recites, inter alia:
wherein generating the scaled time differential comprises applying the time differential to a logarithmic scaling function. (This is mental process of observation, evaluation and judgment wherein a user performs a mathematical operation using the time difference.)
Step 2A Prong 2:
This judicial exception is no integrated into a practical application. Aside from the limitations above, there are not additional limitations.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. There are no additional limitations.
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 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.
Claims 1, 3-4, 6 and 19-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Aggarwal et al. (US 2021/0375441 A1 – hereinafter Aggarwal).
In regards to claim 1, Aggarwal discloses a computer-implemented method comprising:
receiving, by one or more processors, high-frequency data associated with a first capture rate and low-frequency data associated with a second capture rate; (Aggarwal para. [0027] cites “The time-series data is measured continuously. The doctor notes are charted at intermittent times. One example includes a multimodal deep neural network that comprises of recurrent units for the time-series and convolution network for the clinical notes.”, wherein the time-series data is high-frequency data a first capture rate (continuously) and doctor notes are low-frequency data at a second capture rate (intermittent). Also see para. [0037] for intermittent recording. For the processor see fig. 8 element 1102.)
generating, by the one or more processors, vectorized low frequency data by converting the low-frequency data using a low-frequency encoding model; and (Aggarwal para. [0040] cites “As shown in FIG. 2, a convolutional approach can be used to extract the textual features from the doctor's notes. For a piece of clinical note N, the CNN takes the word embeddings e=(ei, e2, ... , en) as input and applies ID convolution operations, followed by maxpooling over time to generate a p dimensional feature vector z, which is fed to the fully connected layer alongside the LSTM output from time series signal (described in the next paragraph) for further processing.”, this teaches creating a vector low frequency data (doctor’s notes) using an encoding model (CNN).)
processing, by the one or more processors and utilizing a prediction model, the high-frequency data and the vectorized low-frequency data to generate output data. (Aggarwal para. [0039] cites “In the multimodal model, the goal is to improve the predictions by taking both the time series data x, and the doctor notes n, as input to the network.” And para. [0044] cites “The time series data xt , is modeled using an LSTM as before. In one example, concatenate the attenuated output from the CNN with the LSTM output for the prediction tasks as follows:…”, from this is processes both the high and low frequency vector data to generate output.)
In regards to claim 3, Aggarwal discloses the computer-implemented method of claim 1, wherein processing the high-frequency data and the vectorized low-frequency data to generate output data comprises: processing, utilizing the prediction model processing the high-frequency data and the vectorized low-frequency data to generate at least one state prediction. (Aggarwal para. [0033] cites “Decompensation concerns detecting patients who are physiologically declining. Decompensation is defined as a sequential prediction task where the model makes a prediction at each hour after ICU admission. The target at each hour is to predict the mortality of the patient within a 24-hour time window.”)
In regards to claim 4, Aggarwal discloses the computer-implemented method of claim 1, further comprising: processing, utilizing the prediction model processing the high-frequency data and the vectorized low-frequency data to generate at least one missing data value associated with the high-frequency data. (Aggarwal para. [0033] and [0037-0038] teaches a sequential prediction task for each hour after ICU admission using sensor data and doctors notes. It teaches forecasting whether a person dies in the ICU within the next 24 hours and forecasting each hour the patients status. This is a missing data value as the instant specification in para. [0163] cites “Additionally or alternatively, in some embodiments, the missing data value includes a predicted data value for a subsequent or future timestamp, for example the next data value predicted in a time series.” .)
In regards to claim 6, Aggarwal discloses the computer-implemented method of claim 1, wherein receiving the high-frequency data comprises capturing the high-frequency data utilizing at least one sensor. (Aggarwal para. [0037] teaches capturing time series data from instruments while fig. 2 and para. [0103] teaches the system using sensors.)
In regards to claim 19, it is the apparatus embodiment of claim 1 with similar limitations to that of claim 1 and thus is rejected using the same reasoning as claim 1. The only difference is claim 19 cites a processor and memory which is disclosed by Aggarwal in figure 8 element 1102 for the processor and element 1104 for the memory with instructions.
In regards to claim 20, it is the non-transitory computer readable storage medium embodiment of claim 1 with similar limitations to that of claim 1 and thus is rejected using the same reasoning as claim 1. The only difference is claim 20 cites a non-transitory computer readable storage medium with a processor which is disclosed by Aggarwal in figure 8 element 1102 for the processor and para. [0106] wherein it cites “In some examples, machine readable media may include non-transitory machine-readable media. In some examples, machine readable media may include machine readable media that is not a transitory propagating signal.”
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.
Claims 2 and 7-8 are rejected under 35 U.S.C. 103 as being unpatentable over Aggarwal et al. (US 2021/0375441 A1 – hereinafter Aggarwal) as applied to claim 1 above, and further in view of Snoek et al. (“Early Versus Late Fusion in Semantic Video Analysis” – hereinafter Snoek).
In regards to claim 2, Aggarwal discloses the computer-implemented method of claim 1, but fails to explicitly disclose further comprising:
receiving, by one or more processors, output truth source data; generating, by the one or more processors, a plurality of variants of the prediction model, wherein each variant predicts the output truth source data based at least in part on the high-frequency data by at least: (1) introducing the vectorized low frequency data in combination with the high-frequency data at a different point in the training for each variant of the plurality of variants of the prediction model, and (2) completing the training upon introduction of the vectorized low frequency data; and selecting, by the one or more processors, an optimal variant from the plurality of variants of the prediction model based at least in part on performance data corresponding to each variant of the plurality of variants, wherein the prediction model utilized to generate the output data comprises the optimal variant.
Snoek discloses receiving, by one or more processors, output truth source data; (Snoek page 401 section 2.1 and 4.2 teaches annotate a ground truth for data.) generating, by the one or more processors, a plurality of variants of the prediction model, wherein each variant predicts the output truth source data based at least in part data by at least: (Snoek abstract teaches generating a model using early fusion and late fusion, wherein each model is a variant) (1) introducing the vectorized data in combination with other vectorized data at a different point in the training for each variant of the plurality of variants of the prediction model, (Snoek abstract, figure 1 and figure 2 teaches combining various types of vectorized data using early fusion and one using late fusion, wherein early and late are both different points in training.) and (2) completing the training upon introduction of the vectorized data; (Snoek page 401 and section 4.1 and 5 teaches models were trained using the vectorized data, thus it was completed after introducing vectorized data) and selecting, by the one or more processors, an optimal variant from the plurality of variants of the prediction model based at least in part on performance data corresponding to each variant of the plurality of variants, wherein the prediction model utilized to generate the output data comprises the optimal variant. (Snoek section 5 teaches comparing the models for better scoring on 14 concepts. It cites “More surprising is the result for the early fusion scheme, which obtains a better score for 6 concepts. Here, the absolute difference ranges from 0.0 for weather news to 0.3 for stock quotes…For concepts road and ice hockey, the late fusion scheme is able to improve results. From the scores for the visual modality and the textual modality we observe that for the concepts road and ice hockey the scores either form a nice cluster (road) or are easily separable (ice hockey), see Figure 6 for ice hockey results. For stock quotes the situation is different. The late fusion scheme classifies a large number of easily separable scores correctly….These results suggest that a fusion strategy on a per-concept basis yields the most effective semantic index.”. As shown in the citations the optimal variant is based on the concept and picking the best model based on performance would be obvious.)
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to modify the teachings of the Aggarwal with that of the Snoek in order to allow for testing variants of model wherein vectorized data was combined with other data at various points in training as both references deal with using machine learning models and combining various types of data. The benefit of doing so creates a system that more accurate by testing the performance of models to be sure the best model is selected based on the application, as suggest by Snoeck where it cites the optimal or best strategy is on a per-concept basis.
In regards to claim 7, Aggarwal in view of Snoek disclose the computer-implemented method of claim 2, wherein receiving the output truth source data comprises receiving user input indicating at least one state associated with at least one portion of the low-frequency data and at least one portion of the high-frequency data. (Snoeck page 4.1 and 4.2 teaches manually annotated ground truth of concepts, and figures 1 and 2 shows text, video and audio which makes up both low and high frequency data. Low being text and high be audio and video.)
In regards to claim 8, Aggarwal in view of Snoek disclose the computer-implemented method of claim 2, wherein receiving the output truth source data comprises: receiving historical high-frequency data; and deriving the output truth source data based at least in part on the historical high-frequency data. (Aggarwal para. [0027] teaches collecting time-series data which is historical high-frequency data) and para. [0076-0076] teaches supervised learning wherein a model is trained with input data and known correct output for the input data (output truth source data), and then determines if the model gets the correct answer. This deriving the output truth source data base on the historical data. Examiner’s note: The examiner interprets deriving the output truth source data to be getting the ground truth or known correct answer from using the historical data and the model. This support by the instant specification in para. [0167] wherein it cites “For example, in some embodiments, the apparatus 400 processes the historical high-frequency data utilizing a rules engine. The rules engine may process the historical high-frequency data to generate corresponding portion(s) of output truth source data based on the historical high-frequency data.”)
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Aggarwal et al. (US 2021/0375441 A1 – hereinafter Aggarwal) as applied to claim 1 above, and further in view of Aghaei et al. – (US 2022/0351860 A1)
In regards to claim 5, Aggarwal does not explicitly disclose wherein the prediction model comprises a modified U-net architecture model.
Aghaei et al. discloses wherein the prediction model comprises a modified u-net. (Aghaei et al. para. [0099] cites “ At block 520, a prediction model such as a two-dimensional segmentation model is trained using the one or more tile images or the image patches. In some instances, the two-dimensional segmentation model is a modified U-Net model comprising contracting path and an expansive path, each of the contracting path and the expansive path having a maximum of 256 channels, and one or more layers of the contracting path implement spatial drop out.”)
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to modify the teachings of the Aggarwal with that of Aghaei et al. in order to use a modified U-net for predictions as both references using convolutional neural networks (CNN) for making predictions from patient data and U-nets are a type of CNN. The benefit of doing so is it created a robust system is better able to handle different forms of data as it U-nets handle imaging data well and it would allow patient data such as x-rays and scans be used in predictions.
Allowable Subject Matter
Claims 9-18 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter: None of the cited references alone or in combination disclose the limitations in claim 9 which include: wherein generating the vectorized low frequency data by converting the low-frequency data using the low-frequency encoding model comprises: generating a code data vector set by at least converting each unique code in the unique code set to a code data vector; generating a set of time-by-code vectors by at least, for each instance of the unique code in the low-frequency data: determining a time differential between a recordation timestamp associated with the instance of the unique code and a sensed timestamp associated with at least a portion of the high-frequency data; generating a transformed time vector based at least in part on the time differential, wherein the transformed time vector is generated utilizing a time transformation function; concatenate the transformed time vector associated with the instance of the unique code with a code data vector from the code data vector set, wherein the code data vector corresponds to the instance of the unique code; and generating a combined vector by at least applying the set of time-by-code vectors to an attention model that generates the combined vector, wherein the combined vector comprises the vectorized low frequency data.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PAULINHO E SMITH whose telephone number is (571)270-1358. The examiner can normally be reached Mon-Fri. 10AM-6PM CST.
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/PAULINHO E SMITH/Primary Examiner, Art Unit 2127
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