DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Acknowledgments
Claims 1-20 are pending
Applicant provided information disclosure statement.
Claim Objections
Claims 5, 12, and 19 are objected to because of the following informalities: Claims 5, 12, and 19 recite a list but do not provide a comma between different parameters and one or more remote services. Appropriate correction is required.
Allowable Subject Matter
Claims 5, 12, and 19 are allowable if rewritten to include all of the limitations of the base claim and any intervening claims, and if the independent claims were amended in such a way as to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action. The closest prior art to these claims include Beals (US20170146964A1) in further view of White (US20190347621A1) in further view of Pratz (US20200058176A1) in further view of Saveliev (US20160132948A1) who teaches service parameters with respect to different service providers. However, with respect to exemplary claim 5, 12, and 19, the closest prior art of record, either alone or taken in combination with any other references of record, do not anticipate or render obvious the claimed functionality of claim 5, 12, and 19.
Claims 6, 13, and 20 are allowable if rewritten to include all of the limitations of the base claim and any intervening claims, and if the independent claims were amended in such a way as to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action. The closest prior art to these claims include Beals (US20170146964A1) in further view of White (US20190347621A1) in further view of Pratz (US20200058176A1) in further view of Alkan (US20210150381A1) who teaches predicting user availability. However, with respect to exemplary claim 6, 13, and 20 the closest prior art of record, either alone or taken in combination with any other references of record, do not anticipate or render obvious the claimed functionality of claim 6, 13, and 20.
Claims 7 and 14 are allowable if rewritten to include all of the limitations of the base claim and any intervening claims, and if the independent claims were amended in such a way as to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action. The closest prior art to these claims include Beals (US20170146964A1) in further view of White (US20190347621A1) in further view of Pratz (US20200058176A1) in further view of Abhinav (US20190122162A1) who teaches selecting recommended tasks based on predicted interest of a resource. However, with respect to exemplary claim 7 and 14 the closest prior art of record, either alone or taken in combination with any other references of record, do not anticipate or render obvious the claimed functionality of claim 7 and 14.
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 a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more than the judicial exception itself.
Regarding Step 1 of subject matter eligibility for whether the claims fall within a statutory category (See MPEP 2106.03), claims 1-20 are directed to non-transitory computer-readable medium, system, and method.
Regarding step 2A-1, Claims 1-20 recite a Judicial Exception. Exemplary independent claim 1 and similarly claims 8 and 15 recite the limitations of
Receiving…sensor data characterizing a state of an environment; detecting an environment condition by comparing the sensor data to one or more thresholds…retrieving features from a service associated with a user, wherein the environment is configured to be managed by the user; executing a…model using the sensor data and the features, wherein the…model generates one or more implementations of a task configured to address the environment condition; receiving authorization to execute an implementation of the task; receiving feedback associated with executing the implementation of the task; and generating a training dataset associated with the…model by appending the feedback to training data used to train the…model…
These limitations, as drafted, are a process that, under its broadest reasonable interpretation cover concepts of receiving, retrieving, detecting, executing, generating, and appending data. The claim limitations fall under the abstract idea grouping of a mental process, because the limitations can be performed in the human mind, or by a human using a pen and paper. For example, but for the language of a system and non-transitory computer-readable medium, the claim language encompasses simply receiving sensor data, detecting an environment condition, retrieving features, executing a model to determine an implementation of a task, receiving feedback, and generating a training data set by adding feedback data for another execution of the model. These are mere data manipulation steps that do not require a computer. For example, a user can run a model a plurality of times and change the data each iteration. A user can determine a task implementation and determine when there is a hardware fault by analyzing received data. The claimed invention is merely automating a manual process.
The claims recite generating a task implementation with respect to a hardware fault. The Applicant’s specification also recites generating task recommendations in para 0003. The claimed invention clearly teaches task management and these tasks are carried out by businesses as seen in para 0123 of Applicant’s specification. These make the claims fall in the abstract idea grouping of certain methods of organizing human activity (fundamental economic principles or practices; business relations, interactions between people). It is clear the limitations recite these abstract idea groupings, but for the recitations of generic computer components. The mere nominal recitations of generic computer components do not take the limitations out of the mental process and certain methods of organizing human activity grouping. The claims are focused on the combination of these abstract idea processes.
Regarding step 2A-2- This judicial exception is not integrated into a practical application, and the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The claim recites the additional elements of sensors, machine learning model, system, processors, and non-transitory computer readable medium.
These components are recited at a high level of generality and merely automate the steps. Each of the additional limitations is no more than mere instructions to apply the exception using a generic computer component.
The combination of these additional elements is no more than mere instructions to apply the exception using a generic computer components or software. Accordingly, even in combination, 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.
Further, the claims do not provide for recite any improvements to the functioning of a computer, or to any other technology or technical field; applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition; applying the judicial exception with, or by use of, a particular machine; effecting a transformation or reduction of a particular article to a different state or thing; or applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
The dependent claims have the same deficiencies as their parent claims, as being directed towards an abstract idea, as the dependent claims merely narrow the scope of their parent claims. For example, the dependent claims further describe how the sensor data is received such as stating the additional element of a device. In addition, the dependent claims recite additional abstract idea steps such as predicting.
Regarding step 2B the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because claim 1 recites
Sensor, machine learning model
Claim 2, 9, and 16 recite devices
Claims 3, 10, and 17 recite Internet-of-Things devices
Claims 4, 11, and 18 recite home automation device
Claim 8 recites system, processors, non-transitory computer-readable medium, sensor, machine learning model
Claim 15 recites non-transitory computer-readable medium, processors, sensor, machine learning model
When looking at these additional elements individually, the additional elements are purely functional and generic the Applicant specification states a general-purpose processor in para 0224.
When looking at the additional elements in combination, the Applicant’s specification merely states a general-purpose processor as seen in para 0224. The computer components add nothing that is not already present when the steps are considered separately. See MPEP 2106.05
Looking at these limitations as an ordered combination and individually adds nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use generic computer components, recitations of generic computer structure to perform generic computer functions that are used to "apply" the recited abstract idea. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claim as a whole amounts to significantly more than the abstract idea itself. Since there are no limitations in these claims that transform the exception into a patent eligible application such that these claims amount to significantly more than the exception itself, claims 1-20 are rejected under 35 U.S.C. 101.
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.
Claim(s) 1, 2, 3, 4, 8, 9, 10, 11, 15, 16, 17, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Beals (US20170146964A1) in further view of White (US20190347621A1) in further view of Pratz (US20200058176A1).
Regarding claim 1 and similarly claim 8 and 15, Beals teaches
A method comprising (See para 0002-In some embodiments, a method may be provided including receiving, at an electronic device, a temperature value) This teaches a method.
A system comprising: one or more processors; and a non-transitory computer-readable medium storing instructions that when executed by the one or more processors, cause the one or more processors to perform operations including: (See figures 1-4 and 9) (See para 0010-Embodiments of such a system may include one or more of the following features: The memory further may have stored therein processor-readable instructions which, when executed by the one or more processors, cause the one or more processors) This shows a processor and memory.
A non-transitory computer-readable medium storing instructions that when executed by one or more processors, cause the one or more processors to perform operations including (See fig. 9) (See para 0137-The computer system 900 may further include and/or be in communication with one or more non-transitory storage devices 925) This shows non-transitory memory.
receiving, from one or more sensors, sensor data characterizing a state of an environment (See para 0002-receiving, at an electronic device, a temperature value from a temperature sensor located at a vent grate of an HVAC system, the temperature value indicating the temperature of the air emitting from the HVAC system. The method may include receiving, at the electronic device, an airflow value from a pressure sensor located at the vent grate of the HVAC system, the airflow value determined by the pressure differential of the air emitting from the HVAC system) This shows receiving sensor data. Sensor data corresponds to the state of environment such as temperature and airflow in the environment.
detecting an environment condition by comparing the sensor data to one or more thresholds, wherein the environment condition is indicative of a hardware fault (See para 0002-The method may include calculating, at the electronic device, a temperature delta value based on the temperature value and an ambient temperature value. The method may include comparing, at the electronic device, the temperature delta value with a predetermined temperature delta threshold value, wherein the predetermined temperature delta threshold value is stored in a storage device communicatively coupled to the electronic device. The method may include comparing, at the electronic device, the airflow value with a predetermined airflow threshold value, wherein the predetermined airflow threshold value is stored in the storage device…an error with the HVAC system. The method may include sending a notification of the error from the electronic device to a user.) This shows the sensor values are compared to thresholds with respect to the temperature and airflow to determine an error with the HVAC system (i.e. hardware fault).
retrieving features from a service associated with a user, wherein the environment is configured to be managed by the user (See para 0050-Home automation settings database 247 may allow configuration settings of home automation devices and user preferences to be stored. Home automation settings database 247 may store data related to various devices that have been set up to communicate with television receiver 200. For instance, home automation settings database 247 may be configured to store information on which types of events should be indicated to users, to which users, in what order, and what communication methods should be used.) (See para 0052- Therefore, the user may configure…lower a heat setting of thermostat,) This shows the system retrieves user features (i.e. user settings/preferences) with respect to a service such as automation service. This also shows the environment is configured by the user such as setting the thermostat as seen in para 0052.
using the sensor data and the features…generates one or more implementations of a task configured to address the environment condition (See para 0114- For example, if the error identified is a degraded filter, the television receiver 200 can send a notification to the user stating that there is an error, that the error is the result of a degraded filter, and/or that the user should replace the filter. In some embodiments, the notification can be sent, for example, via email, text, and/or displayed on a television in house 530.) This shows the system generates an implementation of a task such as replacing a filter to address the environment condition of a faulty HVAC system. This is determined with respect to the sensor data to determine a fault and user features in communicating the fault and task.
Even though Beals teaches determining and generating a task, it doesn’t do this with respect to machine learning, however White teaches executing a machine-learning model…wherein the machine-learning model generates…(See para 0008-Innovations described herein also generally pertain to training machine-learned algorithms using large-scale appointment data to estimate task durations) (See para 0142- Further, in a particularly advantageous implementation, machine learning and/or a trainable algorithm may be employed to provide this operability, in whole or in part.) This shows machine learning models are used.
Beals and White are analogous art because they are from the same problem-solving area of tasks. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Beals invention by incorporating the method of White because Beals could also use machine learning when generating tasks. This would make the art of Beals more sophisticated since the machine learning models would be able to handle complex data sets and it would provide more accurate determination of tasks since machine learning models optimize over time.
White further teaches details about the machine learning such as receiving feedback associated with executing the implementation of the task (See abstract-The duration estimate may then be presented to a user and feedback as to the accuracy of the estimate may be collected. The feedback may, in turn, be added to the historical data to improve subsequent time estimates) This teaches that feedback is received with respect to implementation of a task such as the associated task duration.
and generating a training dataset associated with the machine-learning model by appending the feedback to training data used to train the machine-learning model (See abstract-The feedback may, in turn, be added to the historical data to improve subsequent time estimates) This shows that feedback data is added to historical dataset. The historical data is used by machine learning with respect to training as seen here (See para 0157-Here, it is to be appreciated that deep learning architectures, such as neural networks and the like, are particularly well-suited to identifying patterns in data sets. Consequently, expanding the historical data (and its distribution) helps deep learning architectures learn. This represents training of such architectures.) (See para 0141-Further, it is to be appreciated that this distributional information can be useful to build computational models for task duration estimation. Additionally, machine-learned algorithms are trainable using large-scale appointment data to estimate task durations.)
wherein generating the training dataset causes a modification in a subsequent iteration of the machine-learning model, and wherein the modification improves generation of a subsequent task implementation. (See abstract-The feedback may, in turn, be added to the historical data to improve subsequent time estimates while respecting and protecting user privacy.) (See para 0052-Also, the innovative approach is trainable in that it uses feedback about the accuracy of duration estimates in subsequent estimates to improve future performance.) (See para 0086- receive one or more automated time estimate(s) of how much time a task will take to complete, and to provide feedback as to the accuracy of the time estimate(s), which enhances the accuracy of subsequent time estimates. This feedback also promotes machine learning.) This shows the subsequent iteration will be modified since the historical data will be updated and used. This also will improve the accuracy with respect to task implementation by way of determining more accurate task durations.
Beals and White are analogous art because they are from the same problem-solving area of tasks. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Beals invention by incorporating the method of White because Beals could also use machine learning when generating tasks. This would make the art of Beals more sophisticated since the machine learning models would be able to handle complex data sets and it would provide more accurate determination of tasks since machine learning models optimize over time.
In addition, even though Beals teaches tasks it doesn’t teach authorization to execute a task, however Pratz teaches
receiving authorization to execute an implementation of the task (See para 0029- In some example embodiments, the smart contract specifies hardware appliance tasks that can be automatically or manually approved for completion (e.g., via a service provider of the hardware appliance).) This shows approval/authorization for a task.
Beals and Pratz are analogous art because they are from the same problem-solving area of tasks. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Beals invention by incorporating the method of Pratz because Beals could also have an additional step of authorizing a task. Having this additional step would make sure that the system provides an accurate task to the user. The user could also have an authorizing step with respect to the task and provide feedback if the task is not the correct task. This ensures the correct task is carried out to address the hardware fault.
Regarding claim 2 and similarly claims 9 and 16, Beals, White, and Pratz teach the limitations of claim 1, 8, and 15, however Beals further teaches
wherein the sensor data is received using a data model that enables communication between two or more devices. (See para 0082- In use, HVAC Health Monitoring System 300 can be used to communicate information regarding the HVAC system (e.g., HVAC system 500; FIG. 5) to an electronic device (e.g., television receiver 200; FIG. 2) for inspection.) This shows sensor data is received with a model that includes communication with two devices such as HVAC monitoring system and television receiver. Figure 1 also shows communication between multiple devices.
Regarding claim 3 and similarly claims 10 and 17, Beals, White, and Pratz teach the limitations of claim 1, 8, and 15, however Beals further teaches
wherein the one or more sensors are connected to one or more Internet-of-Things devices. (See para 0081- HVAC Health Monitoring Device 340 can be wired or wirelessly coupled to components including, for example, pressure sensor 335, temperature sensor 330, carbon monoxide sensor 325, and ambient temperature sensor 320. HVAC Health Monitoring Device 340 can also include a communication component (not shown) that can wirelessly communicate with an electronic device, such as, for example television receiver 200 (FIG. 2) …HVAC Health Monitoring Device 340 can communicate with the electronic device through a wired connection or wirelessly. If communicating wirelessly, any suitable wireless communication protocol can be used, including, for example, Bluetooth®, ZigBee®, any of the IEEE 802.11 family of wireless protocols, or any other wireless protocol.) This shows the sensors are connected to item 340 which is an internet of things device since it is capable of wireless connections (e.g. Wi-Fi).
Regarding claim 4 and similarly claims 11 and 18, Beals, White, and Pratz teach the limitations of claim 1, 8, and 15, however Beals further teaches
wherein the one or more sensors are connected to a home automation device. (See para 0027- 0028- Embodiments detailed herein present an HVAC health monitoring device that can be installed in a vent grate of an HVAC system…In some embodiments, the device can be installed as part of a home automation system. The device can communicate with an electronic device, for example, a home automation control unit. The home automation control unit can receive data (e.g., the sensor output) from the device and utilize the data to determine whether the HVAC system is experiencing issues.) This shows the sensors which are part of the HVAC health monitoring device are connected to a home automation system.
Conclusion
The prior art made of record and not relied upon considered pertinent to Applicant’s disclosure.
Saveliev (US20160132948A1) teaches service parameters with respect to different service providers.
Alkan (US20210150381A1) who teaches predicting user availability
Abhinav (US20190122162A1) who teaches selecting recommended tasks based on predicted interest of a resource
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MUSTAFA IQBAL whose telephone number is (469)295-9241. The examiner can normally be reached Monday Thru Friday 9:30am-7:30 CST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Beth Boswell can be reached at (571) 272-6737. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MUSTAFA IQBAL/Primary Examiner, Art Unit 3625
Prosecution Insights