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 .
DETAILED ACTION
1. This initial office action is based on the application filed on 08/16/2024, which claims 1-20 have been presented for examination.
Status of Claim
2. Claims 1-20 are pending in the application and have been examined below, of which, claims 1, 9 and 17 are presented in independent form.
Priority
3. This application is a CON of 17/053,732 filed on 11/06/2020 PAT 12093675 which is a 371 of PCT/US2018/047249 filed on 08/21/2018 which claims benefit of 62/667,959 filed on 05/07/2018.
Information Disclosure Statement
4. The information disclosure statement (IDS) submitted on 06/11/2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Examiner Notes
5. Examiner cites particular columns and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
Abstract Objection
6. Abstract, line 1, recites “The present disclosure”. Applicant is reminded of the proper language and format for an abstract of the disclosure.
The abstract should be in narrative form and generally limited to a single paragraph on a separate sheet within the range of 50 to 150 words in length. The abstract should describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text for details.
The language should be clear and concise and should not repeat information given in the title. It should avoid using phrases which can be implied, such as, “The disclosure concerns,” “The disclosure defined by this invention,” “The disclosure describes,” etc. In addition, the form and legal phraseology often used in patent claims, such as “means” and “said,” should be avoided.
Claim Objections
7. Claims 9-16 are objected to because of the following informalities:
Claim 9 recites the limitation "a memory" in lines 7-8. There is insufficient antecedent basis for this limitation in the claim.
Claims 10-16 depend on claim 9 are also objected.
Appropriate correction is required.
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.
8. 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 analysis specific to Claims 1, 9 and 17 is being presented below.
Claims 1, 9 and 17:
Step 1 Analysis:
Claims 1-8 of the instant application is direct to apparatus/device.
Claims 9-16 of the instant application is direct to process/method.
Claims 17-20 of the instant application is direct to product/computer-readable media.
Thus, they are statutory categories.
Step 2 Analysis:
Claims 1 and 17 recite:
(a) store one or more machine-learned models and a machine learning library;
(b) receiving, from a mobile computing device, data indicating that a computer application stored in a memory of the mobile computing device has begun execution, the data including input data from the computer application;
(c) identifying a first machine-learned model of the one or more machine-learned models and machine learning library based at least in part on the input data from the computer application;
(d) communicating, using an application programming interface, the first machine-learned model to the mobile computing device.
Step 2A -- Prong 1:
The claim 1 recites the limitations of:
(c) identifying a first machine-learned model of the one or more machine-learned models and machine learning library based at least in part on the input data from the computer application;
Limitation (c) limitations that, as drafted, are processes that, under its broadest reasonable interpretations, cover performance of the limitation in the mind. That is, nothing in the claim elements precludes the step from practically being performed in the mind or with a pen and paper, i.e. “identifying” can be performed in the human mind through observation, evaluation, judgement, opinion with the aid of pen and paper. As such, these limitations fall within the “Mental Processes” grouping of abstract ideas.
Step 2A -- Prong 2:
The claim 1 recites the additional limitations of “a computing device”, “one or more processors”, “one or more non-transitory computer-readable media”, “a mobile computing device” and “a memory”. The limitations of “a computing device”, “one or more processors”, “one or more non-transitory computer-readable media”, “a mobile computing device” and “a memory” are recited at a high level of generality, i.e., merely instructions to implement the abstract idea on a generic computer or merely uses a computer as a tool to perform the abstract idea. The limitations “a machine intelligence software development kit” and “a first machine-learned model of the one or more machine-learned models and machine learning library” recited as tools perform abstract idea. Additionally, limitations (a), (b) and (d) perform as well-understood, routine and conventional activity. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2 Analysis:
Claim 9 recites:
(a) receiving, by one or more processors of a computing device, data from a mobile computing device, the data indicating that a computer application stored in a memory of the mobile computing device has begun execution, the data including input data from the computer application;
(b) identifying, by the one or more processors, a first machine-learned model of one or more machine-learned models and a machine learning library stored in a memory of the computing device, the first machine-learned model being identified based at least in part on the input data from the computer application;
(c) communicating, by the one or more processors, the first machine-learned model to the mobile computing device using an application programming interface.
Step 2A -- Prong 1:
The claim 9 recites the limitations of:
(b) identifying, by the one or more processors, a first machine-learned model of one or more machine-learned models and a machine learning library stored in a memory of the computing device, the first machine-learned model being identified based at least in part on the input data from the computer application;
Limitation (b) limitations that, as drafted, are processes that, under its broadest reasonable interpretations, cover performance of the limitation in the mind. That is, nothing in the claim elements precludes the step from practically being performed in the mind or with a pen and paper, i.e. “identifying” can be performed in the human mind through observation, evaluation, judgement, opinion with the aid of pen and paper. As such, these limitations fall within the “Mental Processes” grouping of abstract ideas.
Step 2A -- Prong 2:
The claim 9 recites the additional limitations of “one or more processors of a computing device”, “a mobile computing device” and “a memory of the computing device”. The limitations of “one or more processors of a computing device”, “a mobile computing device” and “a memory of the computing device” are recited at a high level of generality, i.e., merely instructions to implement the abstract idea on a generic computer or merely uses a computer as a tool to perform the abstract idea. The limitation “a first machine-learned model of one more machine-learned models and machine learning library” recited as tools perform abstract idea. Additionally, limitations (a) and (c) perform as well-understood, routine and conventional activity. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B:
As explained with respect to Step 2A Prong Two, the additional elements in the claim are recited at a high level of generality and amount to no more than mere instructions to apply the exception using generic computer components. Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The same analysis applies here in 2B, i.e., simply adding extra-solution activity or well-understood, routine and conventional activity or generic computer components does not integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B since the courts have identified functions such as gathering, displaying, updating, transmitting/receiving/communicating and storing data as well- understood, routine, conventional activity. See MPEP 2106.05(d) and See MPEP 2106.05(g) . Therefore, claims are ineligible.
Dependent claims
Additionally, claims 2, 10 and 18 recite “receiving, from the mobile computing device, data indicative of one or more device capabilities of the mobile computing device; and identifying the first machine-learned model based at least in part on the data indicative of the one or more device capabilities” as drafted, is a process that, under its broadest reasonable interpretations, covers performance of the limitation in the mind. That is, nothing in the claim elements precludes the step from practically being performed in the mind or with a pen and paper, i.e. “receiving” and “identifying” can be performed in the human mind through observation, evaluation, judgment, opinion with the aid of pen and paper. As such, this limitation falls within the “Mental Processes” grouping of abstract idea. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or providing an inventive concept and thus do not amount to significantly more than the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claims 2, 10 and 18 are ineligible.
Additionally, claims 3, 11 and 19 recite “receiving, from the mobile computing device, data indicative of model performance of machine-learned models on the mobile computing device; and modifying at least one model of the one or more machine-learned models based at least in part on the data indicative of model performance of machine-learned models on the mobile computing device” which perform as well-understood, routine and conventional activity. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or providing an inventive concept and thus do not amount to significantly more than the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claims 3, 11 and 19 are ineligible.
Additionally, claims 4 and 12 recite “wherein the data indicative of model performance of machine-learned models on the mobile computing device comprises data indicative of on-device inference and training of the at least one model” is merely insignificant extra solution activity of defining data. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or providing an inventive concept and thus do not amount to significantly more than the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claims 4 and 12 are ineligible.
Additionally, claims 5 and 13 recite “wherein the data indicative of model performance of machine-learned models comprises user-specific data, and wherein modifying the at least one model comprises performing personalization of the at least one model based at least in part on the user-specific data” is merely insignificant extra solution activity of performing data. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or providing an inventive concept and thus do not amount to significantly more than the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claims 5 and 13 are ineligible.
Additionally, claims 6 and 14 recite “wherein modifying the at least one model comprises compressing the at least one model” which perform as well-understood, routine and conventional activity. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or providing an inventive concept and thus do not amount to significantly more than the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claims 6 and 14 are ineligible.
Additionally, claims 7, 13 and 20 recite “receiving, from the mobile computing device, training data for the first machine-learned model; training at least one model of the one or more machine-learned models using the training data to generate a trained model; and providing the trained model as the first machine-learned model”. The limitation recites “receiving, from the mobile computing device, training data for the first machine-learned model” which perform as well-understood, routine and conventional activity. The limitations “training at least one model of the one or more machine-learned models using the training data to generate a trained model; and providing the trained model as the first machine-learned model” are merely insignificant extra solution activity of generating data. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or providing an inventive concept and thus do not amount to significantly more than the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claims 7, 13 and 20 are ineligible.
Additionally, claims 8 and 16 recite “wherein communicating the first machine-learned model comprises providing, via the application communication interface, a universal resource locator for downloading the first machine-learned model” which perform as well-understood, routine and conventional activity. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or providing an inventive concept and thus do not amount to significantly more than the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claims 8 and 16 are ineligible.
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.
9. Claim(s) 1, 3-5, 7, 9, 11-13, 15, 17 and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Aradhye et al. (US Patent No. 8,510,238 B1 – IDS filed on 06/11/2025 – herein after Aradhye) in view of He et al. (US pub. No. 2019/0086988 A1 – IDS filed on 06/11/2025 -- herein after He).
Regarding claim 1.
Aradhye discloses
A computing device (mobile platform – See Fig. 5A), comprising:
one or more processors (a processor – See col. 2, lines 9-13); and
one or more non-transitory computer-readable media that collectively store (the article of manufacture includes a non-transitory computer-readable storage medium having instructions stored thereon that, when executed on by a processor, cause the processor to perform functions - See col. 2, lines 24-53):
a machine intelligence software [[development]] kit (applications 230, 240 can use interface(s) to machine learning and adaptation service 220 to permit use of machine learning and adaptation service 220 as a toolkit of machine-learning techniques – See col. 11, lines 50-55), the machine intelligence software [[development]] kit configured to (applications 230, 240 can use interface(s) to machine learning and adaptation service 220 to permit use of machine learning and adaptation service 220 as a toolkit of machine-learning techniques - See col. 11, lines 32-61):
store one or more machine-learned models and a machine learning library (models for machine learning and adaptation can be saved and loaded via machine learning and adaptation service API 310 - See col. 11, lines 62-67 and col. 12, lines 1- 6); and instructions that, when executed by the one or more processors, cause the one or more processors to perform operations, the operations comprising:
receiving, from a mobile computing device, data indicating that a computer application stored in a memory of the mobile computing device has begun execution (Method 150 begins at block 160, where a machine-learning service executing on a mobile platform can receive feature-related data. The feature-related data can include data related to a first plurality of features received from an application executing on the mobile platform – See col. 9, lines 60-64), the data including input data from the computer application (feature-related data can include commands to machine learning and adaptation service 220, such as a command to "train" or learn about input data and perform one or more machine learning operations on the learned input data – See col. 11, lines 20-24);
[[identifying]] a first machine-learned model of the one or more machine-learned models and machine learning library based at least in part on the input data from the computer application (As shown in FIG. 5A, smart microphone setting 526 can learn and set microphone volume based on called party and can be set to either enabled or disabled. When smart microphone setting 526 is disabled, dialer application 520 can instruct a microphone application (not shown in FIG. 5A) to use manual microphone setting 524 to determine output volume for a microphone of mobile platform 502. When smart microphone setting 526 is enabled, dialer application 520 can use a learning service of machine learning and adaptation service 220 to perform a machine-learning operation to provide setting values for the microphone setting. Then, upon receiving a setting value, dialer application 520 can provide the setting value to the microphone application, which can then determine output volume for the microphone of mobile platform 502 using the setting value. In scenario 500, and as shown in FIG. 5A, smart microphone setting 526 is set to enabled – See col. 17, lines 51-67); and
communicating, using an application programming interface, the first machine-learned model to the mobile computing device (The machine-learning service can communicate with software applications via an Application Program Interface (API). The API provides access to several commonly-used machine adaptation techniques. For example, the API can provide access to interfaces for ranking, clustering, classifying, and prediction techniques. Also, a software application can provide one or more inputs to the machine-learning service. For example, a software application controlling a volume setting of a speaker can provide volume setting values as an input to the machine-learning service – See col. 7, lines 48-57. In specific of the particular embodiments, receiving a selection related to the machine-learning algorithm from the application can include receiving a selection related to the machine-learning algorithm from the application via an Application Programming Interface (API) of the machine-learning service – col. 9, lines 16-21).
Aradhye discloses a toolkit of machine-learning techniques --See col. 11, lines 50-55. Use a learning service of machine learning and adaptation service 220 to perform a machine-learning operation to provide setting values for the microphone setting – See col. 17, lines 51-67. Aradhye does not disclose a machine intelligence software development kit and identifying a first machine-learned model…
He discloses
a machine intelligence software development kit (The machine learning stack may include a software development kit (SDK) that includes one or more Application Programming Interfaces (APIs) that may be used by applications on the wireless communication device to interact with a smart engine – See paragraph [0022]);
identifying a first machine-learned model of the one or more machine-learned models and machine learning library based at least in part on the input data from the computer application (Different machine learning models may be associated with different expected resource use. For example, a first machine learning model may use less battery power, processor time, memory, and/or network bandwidth, etc., and a second machine learning model may use more battery power, processor time, memory, and/or network bandwidth, etc. The smart engine may select a machine learning model based on the determined device status to match an expected resource use of the selected machine learning model with the current resource capacity of the wireless communication device – See paragraph [0024]).
It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to use He's teaching into Aradhye's invention because incorporating He's teaching would enhance Aradhye to enable to provide software development kit (SDK) that includes one or more Application Programming Interfaces (APIs) that may be used by applications on the wireless communication device as suggested by He (paragraph [0022]).
Regarding claim 3, the computing device of claim 1, the operations further comprising:
Aradhye discloses
receiving, from the mobile computing device, data indicative of model performance of machine-learned models on the mobile computing device (The machine-learning service generates an output by performing a machine-learning operation on the at least one feature of the plurality of features. The machine-learning operation is selected from among: an operation of ranking the at least one feature, an operation of classifying the at least one feature, an operation of predicting the at least one feature, and an operation of clustering the at least one feature – See col. 1, lines 39-45); and
modifying at least one model of the one or more machine-learned models based at least in part on the data indicative of model performance of machine-learned models on the mobile computing device (update the model as needed based on feature-related data 222, 232, 242 – See col. 11, lines 34-49).
Regarding claim 4, the computing device of claim 3,
Aradhye discloses
wherein the data indicative of model performance of machine-learned models on the mobile computing device comprises data indicative of on-device inference and training of the at least one model (Each of the learning interfaces shown in learning API 412 can include at least the functions of example learning interface 440. FIG. 4 shows learning interface 440 with five functions: Push( Pull( Notify(), Save(), and Load(). In some embodiments, more or fewer functions can be part of learning interface 440 - See col. 16, lines 4-46. Data from applications and the mobile platform system and stores the aggregated data in a model accessible to machine learning and adaptation engine and aggregation of data enables discovery of new inferences based on the combined data that were difficult to observe in the raw data - See col. 13, lines 10-30).
Regarding claim 5, the computing device of claim 3,
Aradhye discloses
wherein the data indicative of model performance of machine-learned models comprises user-specific data (by providing application access to a number of machine adaptation techniques designed to operate on and learn about user behavior of a mobile platform, the machine-learning service can make mobile platforms easier to use, more efficient from a user's point of view, and save users time and effort in utilizing the variety of applications available on the mobile platform – See col. 8, lines 39-45), and wherein modifying the at least one model comprises performing personalization of the at least one model based at least in part on the user-specific data.
Regarding claim 7, the computing device of claim 1, the operations further comprising:
He discloses
receiving, from the mobile computing device, training data for the first machine-learned model (train a particular machine learning model based on the received data, and may perform future decisions and/or predictions based on the learned machine learning model – See paragraph [0042]);
training at least one model of the one or more machine-learned models using the training data to generate a trained model (a data input to a model that has a high contribution weight to a final score for a decision, prediction, and/or inference, and a non-dominant factor corresponds to a data input to a model that has a low contribution weight to a final score for a decision, prediction, and/or inference – See paragraphs [0058-0060]); and
providing the trained model as the first machine-learned model (select a machine learning model based on the determined device status to match an expected resource use of the selected machine learning model with the current resource capacity of the wireless communication device – See paragraphs [0023-0025]).
It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to use He's teaching into Aradhye's invention because incorporating He's teaching would enhance Aradhye to enable to perform future decisions and/or predictions based on the learned machine learning model as suggested by He (paragraph [0042]).
Regarding claim 9.
Aradhye discloses
A computer-implemented method, the method comprising:
receiving, by one or more processors of a computing device, data from a mobile computing device (A machine-learning service executing on a mobile platform receives data related to a plurality of features – See col. 1, lines 35-46), the data indicating that a computer application stored in a memory of the mobile computing device has begun execution (Method 150 begins at block 160, where a machine-learning service executing on a mobile platform can receive feature-related data. The feature-related data can include data related to a first plurality of features received from an application executing on the mobile platform – See col. 9, lines 60-64), the data including input data from the computer application (feature-related data can include commands to machine learning and adaptation service 220, such as a command to "train" or learn about input data and perform one or more machine learning operations on the learned input data – See col. 11, lines 20-24);
[[identifying]], by the one or more processors, a first machine-learned model of one or more machine-learned models and a machine learning library stored in a memory of the computing device, the first machine-learned model being identified based at least in part on the input data from the computer application (As shown in FIG. 5A, smart microphone setting 526 can learn and set microphone volume based on called party and can be set to either enabled or disabled. When smart microphone setting 526 is disabled, dialer application 520 can instruct a microphone application (not shown in FIG. 5A) to use manual microphone setting 524 to determine output volume for a microphone of mobile platform 502. When smart microphone setting 526 is enabled, dialer application 520 can use a learning service of machine learning and adaptation service 220 to perform a machine-learning operation to provide setting values for the microphone setting. Then, upon receiving a setting value, dialer application 520 can provide the setting value to the microphone application, which can then determine output volume for the microphone of mobile platform 502 using the setting value. In scenario 500, and as shown in FIG. 5A, smart microphone setting 526 is set to enabled – See col. 17, lines 51-67); and
communicating, by the one or more processors, the first machine-learned model to the mobile computing device using an application programming interface (The machine-learning service can communicate with software applications via an Application Program Interface (API). The API provides access to several commonly-used machine adaptation techniques. For example, the API can provide access to interfaces for ranking, clustering, classifying, and prediction techniques. Also, a software application can provide one or more inputs to the machine-learning service. For example, a software application controlling a volume setting of a speaker can provide volume setting values as an input to the machine-learning service – See col. 7, lines 48-57. In specific of the particular embodiments, receiving a selection related to the machine-learning algorithm from the application can include receiving a selection related to the machine-learning algorithm from the application via an Application Programming Interface (API) of the machine-learning service – col. 9, lines 16-21).
. Aradhye discloses use a learning service of machine learning and adaptation service 220 to perform a machine-learning operation to provide setting values for the microphone setting – See col. 17, lines 51-67. Aradhye does not disclose identifying a first machine-learned model…
He discloses
identifying, by the one or more processors, a first machine-learned model of one or more machine-learned models and a machine learning library stored in a memory of the computing device, the first machine-learned model being identified based at least in part on the input data from the computer application (Different machine learning models may be associated with different expected resource use. For example, a first machine learning model may use less battery power, processor time, memory, and/or network bandwidth, etc., and a second machine learning model may use more battery power, processor time, memory, and/or network bandwidth, etc. The smart engine may select a machine learning model based on the determined device status to match an expected resource use of the selected machine learning model with the current resource capacity of the wireless communication device – See paragraph [0024]).
It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to use He's teaching into Aradhye's invention because incorporating He's teaching would enhance Aradhye to enable to provide software development kit (SDK) that includes one or more Application Programming Interfaces (APIs) that may be used by applications on the wireless communication device as suggested by He (paragraph [0022]).
Regarding claim 11, recites the same limitations as rejected claim 3 above.
Regarding claim 12, recites the same limitations as rejected claim 4 above.
Regarding claim 13, recites the same limitations as rejected claim 5 above.
Regarding claim 15, recites the same limitations as rejected claim 7 above.
Regarding claim 17.
Aradhye and He disclose
One or more non-transitory computer-readable media that collectively store:
Regarding claim 17, recites the same limitations as rejected claim 1 above.
Regarding claim 19, recites the same limitations as rejected claim 3 above.
Regarding claim 20, recites the same limitations as rejected claim 7 above.
10. Claim(s) 2, 10 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Aradhye and He as applied to claims 1, 9 and 17 respectively above, and further in view of Jia et al. (US Pub. No. 2019/0073607 A1 – herein after Jia).
Regarding claim 2, the computing device of claim 1, the operations further comprising:
Jia discloses
receiving, from the mobile computing device, data indicative of one or more device capabilities of the mobile computing device (the application may query the client device's hardware capabilities and use it to identify a suitable resource-specific executable that may improve the machine-learning model's performance – See paragraph [0010]); and
identifying the first machine-learned model based at least in part on the data indicative of the one or more device capabilities (different machine-learning models may be configured to be trained or operate using different types of processing hardware, such as CPU, GPU, etc. Rather than designing an app that is pre-integrated with all the different models -- which may have the undesired consequence of enlarging the app size --the app may be configured to integrate with any modularized machine-learning model at run time. For example, by default, an app may be designed to utilize a CPU-based machine-learning model – See paragraphs [0004-0005]).
It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to use Jia's teaching into Aradhye's and He’s inventions because incorporating Jia's teaching would enhance Aradhye and He to enable to query the client device's hardware capabilities and use it to identify a suitable resource-specific executable that may improve the machine-learning model's performance as suggested by Jia (paragraph [0010]).
Regarding claim 10, recites the same limitations as rejected claim 2 above.
Regarding claim 18, recites the same limitations as rejected claim 2 above.
11. Claim(s) 6 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Aradhye and He as applied to claims 3 and 11 respectively above, and further in view of Zhang et al. (US Patent No. 11,182,691 B1 – herein after Zhang).
Regarding claim 6, the computing device of claim 3,
Zhang discloses
wherein modifying the at least one model comprises compressing the at least one model (the input data reaching the MLS may be encrypted or compressed – See col. 10, lines 19-29. Compression metadata 2406, indicating for example the compression algorithm used for the data set, the sizes of the units or blocks in which the compressed data is stored (which may differ from the sizes of the chunks on which chunk-level in-memory filtering operations are to be performed) – See col. 43, lines 43-50).
It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to use Zhang's teaching into Aradhye's and He’s inventions because incorporating Zhang's teaching would enhance Aradhye and He to enable to indicate the compression algorithm in which the compressed data is stored as suggested by Zhang (col. 43, lines 43-50).
Regarding claim 14, recites the same limitations as rejected claim 6 above.
12. Claim(s) 8 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Aradhye and He as applied to claims 1 and 9 respectively above, and further in view of Nookula et al. (US Patent No. 11,544,577 B1 – herein after Nookula).
Regarding claim 8, the computing device of claim 1,
Nookula discloses
wherein communicating the first machine-learned model comprises providing, via the application communication interface, a universal resource locator for downloading the first machine-learned model (provide the user 222 (via response(s) 510) information (e.g., URLs and/or credentials) allowing the user 222 (or the electronic device 102) to obtain the ML model 108 and/or filter(s) 106 – See col. 9, lines 38-41).
It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to use Nookula's teaching into Aradhye's and He’s inventions because incorporating Nookula's teaching would enhance Aradhye and He to enable to provide the user URLs and/or credentials allowing the user to obtain the ML model as suggested by Nookula (col. 9, lines 38-41).
Regarding claim 16, recites the same limitations as rejected claim 8 above.
Conclusion
13. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Blackwood et al. (US Patent No. 11,003,997 B1) discloses machine learning technology for lookalike modeling to expand a list of users with desirable characteristics to a larger list of users which may have similar characteristics. For example, a car company may identify seven thousand profitable customers and want to target content to seven million people who are similar to the seven thousand profitable customers. Example embodiments utilize rich social network data to build and train a model to generate a list of similar users – See col. 1, lines 66-67 and col. 2, lines 1-7.
Deshpande et al. (US Pub. No. 2019/0279114 A1) discloses retrieves from a library of a plurality of machine learning models a machine learning model corresponding to a type of model specified in the one or more parameters. The computing system generates a container image that includes the machine learning model. The computing system provisions a container based on the container image – See Abstract and specification for more details.
Ravi et al. (US Pub. No. 2018/0089588 A1) discloses receive an indication of a user input that selects a candidate response from the one or more candidate responses. Responsive to receiving the indication of the user input that selects the candidate response, the at least one processor may send the candidate response to the external computing device – See Abstract and specification for more details.
Nookula et al. (US Pub. No. 2019/0220783 A1) discloses a request for the generation of the execution plan includes at least one objective for the execution of the ML model and the execution plan is generated based at least in part on comparative execution information and network latency information – See Abstract and specification for more details.
Chan (US Pub. No. 2019/0228261 A1) discloses selecting at least two sets of prebuilt machine learning components from the plurality of sets of prebuilt machine learning components based on one or more implementation rules, the implementation rules indicating a particular platform associated with a system to execute the machine learning application; scoring each of the at least two sets of prebuilt machine learning components; selecting the particular set of prebuilt machine learning components based on the scoring – See paragraph [0009].
Burges et al. (US Patent No. 10,649,739 B2) discloses Input is received from a teacher in association with the module specification. At least a portion of such input indicates how to recognize when a user intends to run an application. An application is generated based on the input provided from the teacher. Such an application can be used by one or more users – See Abstract and specification for more details.
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/MONGBAO NGUYEN/ Examiner, Art Unit 2192
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