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 .
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 non-statutory subject matter.
The claimed invention is directed to an abstract idea without significantly more.
Claim 1 recites a system comprising: a battery processor configured to generate a first battery datum associated with a battery, wherein the battery processor is further configured to transmit the battery datum to merchant processor; a first user processor; and a merchant processor configured to: receive, from the battery processor, a first battery datum associated with a first battery; generate a predictive model based on the first battery datum, wherein the predictive model is configured to predict one or more outcomes associated with the battery; train the predictive model across one or more iterations; update, by the processor, the predictive model with one or more new battery datum; generate, by the predictive model, the one or more outcomes associated with the battery; and transmit an order inquiry to a first user processor, wherein the order inquiry is responsive to the one or more outcomes generated by the predictive model.
Claim 10 recites a method comprising: receiving, from a battery processor, a first battery datum associated with a first battery; generating a predictive model based on the battery datum, wherein the predictive model is configured to predict one or more outcomes associated with the battery; training the predictive model across one or more iterations; updating, by the processor, the predictive model with one or more new battery datum; generating, by the predictive model, one or more outcomes associated with the battery; and transmitting an order inquiry to a first user processor, wherein the order inquiry is responsive to the one or more outcomes generated by the predictive model…
Claim 20 recites a computer readable non-transitory medium comprising computer executable instructions that, when executed on a processor, configure the processor to perform procedures comprising the steps of: receiving, from a battery processor, a first battery datum associated with a first battery; generating a predictive model based on the battery datum, wherein the predictive model is configured to predict one or more outcomes associated with the battery; training the predictive model across one or more iterations; updating, by the processor, the predictive model with one or more new battery datum; generating, by the predictive model, one or more outcomes associated with the battery; and transmitting an order inquiry to a first user processor, wherein the order inquiry is transmitted in response to the one or more outcome generated by the predictive model…
and thus grouped as Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations.
These judicial exceptions are not integrated into a practical application because the additional elements, the data gathering step, (claim 1) “receive, from the battery processor, a first battery datum associated with a first battery” (claim 10) “receiving, from a battery processor, a first battery datum associated with a first battery” (claim 20) “receiving, from a battery processor, a first battery datum associated with a first battery” are mere data gathering that do not add a meaningful limitation to the method as they are insignificant extra-solution activity. Furthermore, the additional elements (claims 1, 10 and 20) the “battery processor, user processor and merchant processor” are recited as performing generic computer functions routinely used in computer applications. Generic computer components recited as performing generic computer functions amount to no more than using a computer as a tool to perform an abstract idea. Additionally, the additional elements (claims 1, 10 and 20), “transmitting an order inquiry to a first user processor,” are considered insignificant extra-solution activity to the judicial exception. All of which are considered not indicative of integration into a practical application (see MPEP 2106.04(d)).
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements of the data gathering steps are mere data collect steps which fall under insignificant extra solution activity and deemed insufficient to qualify as “significantly more” - see MPEP 2106.05(g). The additional elements of the processors are mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and deemed insufficient to qualify as “significantly more” see MPEP 2106.05(f). The additional element, “a quantity related to a horizontal position of an object” is generally linking the use of the judicial exception to a particular technological environment or field of use and deemed insufficient to qualify as “significantly more” see MPEP 2106.05(h)
Dependent claims 2-9 and 11-19 when analyzed as a whole are patent ineligible under 35 U.S.C. §101 because the dependent claims fail to establish that the claims are not directed to an abstract idea as they are directed mathematical concepts and/or mental processes and do not add significantly more to the abstract idea.
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 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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-5 and 7-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Cella et al. [US 2022/0197306 A1; hereinafter “Cella”].
Regarding claim 1, Cella teaches a system (figure 113, prediction, classification, and recommendation chip) comprising:
a battery processor configured to generate a first battery datum associated with a battery, wherein the battery processor is further configured to transmit the battery datum to merchant processor (a battery management system (BMS) and other functions down to a cell level. In other examples, the smart batteries may be smart batteries with cell-level monitoring and data streams - 2426, 2427-2430) (monitoring of power system status at the individual battery level - 2292); a first user processor; and
a merchant processor configured to (receive real-time data from sensor systems of a machinery, vehicle, robot, or other device, and/or sensor systems of the physical environment in which a device operates - 1563): receive, from the battery processor, a first battery datum associated with a first battery ( receive various inputs of any type, including media data such as images/video/audio data, data sets including transaction data, biometric data, motion capture data, pathology data, and/or other such data, and to analyze such data to determine further information (e.g., metadata) about the input data, objects or entities appearing in the input data, and the like – 1700);
generate a predictive model based on the first battery datum, wherein the predictive model is configured to predict one or more outcomes associated with the battery (for use in generating a predictive model - 1701);
train the predictive model across one or more iterations (to train a prediction model for predicting the target variable - 1716);
update, by the processor, the predictive model with one or more new battery datum (optimize the predictive model by updating the training data set, re-training the predictive model - 1718);
generate, by the predictive model, the one or more outcomes associated with the battery ( monitor outcomes associated with classifications, predictions, and/or recommended actions to determine if the classifications and/or predictions were accurate - 1722); and
transmit an order inquiry to a first user processor, wherein the order inquiry is responsive to the one or more outcomes generated by the predictive model (The recommended action and reporting circuit 9536 may transmit the reports to other modules, devices, systems, etc, as outputs – 1721, 1722).
Regarding claim 2, Cella teaches the one or more outcomes comprises at least one selected from the group of battery price, power outage, or future battery needs for the first user (power outages, price increase - 1910).
Regarding claim 3, Cella teaches the merchant processor is further configured to: receive, in response to the order inquiry, an order request from the first user processor; and transmit, in response to the order request, an order confirmation (how a user can request status, observe activity, change a job requirement, respond to an inquiry, and the like - 2259).
Regarding claim 4, Cella teaches the order inquiry comprises at least one selected from the group of a battery replacement, battery recharge, or battery swap (configured to allow a user to research, create, track and report on a technology, development, and/or technology or engineering department initiative including, but not limited to, a new product development, update, enhancement, replacement, upgrade, or the like. – 1175).
Regarding claim 5, Cella teaches the merchant processor is further configured to retrieve historical data associated with the battery from a data storage unit (including historical data on wear-and-tear during usage, historical data on material deterioration under various ambient or environmental conditions - 1909).
Regarding claim 7, Cella teaches the predictive model is a neural network (convolutional neural networks (CNN), deep convolutional neural networks (DCN), feed forward neural networks (including deep feed forward neural networks), recurrent neural networks (RNN) - 1521).
Regarding claim 8, Cella teaches the neural network is at least one selected from the group of an RNN or CNN (convolutional neural networks (CNN), deep convolutional neural networks (DCN), feed forward neural networks (including deep feed forward neural networks), recurrent neural networks (RNN) - 1521).
Regarding claim 9, Cella teaches the merchant processor is further configured to retrieve from a data storage unit battery usage history information associated with the battery processor (including historical data on wear-and-tear during usage, historical data on material deterioration under various ambient or environmental conditions - 1909).
Regarding claim 10, Cella teaches a method comprising: receiving, from a battery processor, a first battery datum associated with a first battery (a battery management system (BMS) and other functions down to a cell level. In other examples, the smart batteries may be smart batteries with cell-level monitoring and data streams - 2426, 2427-2430) (monitoring of power system status at the individual battery level - 2292) (receive real-time data from sensor systems of a machinery, vehicle, robot, or other device, and/or sensor systems of the physical environment in which a device operates - 1563);
generating a predictive model based on the battery datum, wherein the predictive model is configured to predict one or more outcomes associated with the battery (for use in generating a predictive model - 1701);
training the predictive model across one or more iterations (to train a prediction model for predicting the target variable - 1716);
updating, by the processor, the predictive model with one or more new battery datum (optimize the predictive model by updating the training data set, re-training the predictive model - 1718);
generating, by the predictive model, one or more outcomes associated with the battery (monitor outcomes associated with classifications, predictions, and/or recommended actions to determine if the classifications and/or predictions were accurate - 1722); and
transmitting an order inquiry to a first user processor, wherein the order inquiry is responsive to the one or more outcomes generated by the predictive model (The recommended action and reporting circuit 9536 may transmit the reports to other modules, devices, systems, etc, as outputs – 1721, 1722).
Regarding claim 11, Cella teaches receiving, in response to the order inquiry, an order request from the first user processor; and transmitting, in response to the order request, an order confirmation (how a user can request status, observe activity, change a job requirement, respond to an inquiry, and the like - 2259).
Regarding claim 12, Cella teaches the one or more battery outcomes comprises at least a battery transfer from a first user to a second user, a renewal of a battery subscription, or an onsite battery recharge (configured to allow a user to research, create, track and report on a technology, development, and/or technology or engineering department initiative including, but not limited to, a new product development, update, enhancement, replacement, upgrade, or the like. – 1175).
Regarding claim 13, Cella teaches the predictive model analyzes one or more inputs comprising at least battery duration, energy pricing, and battery location (convolutional neural networks (CNN), deep convolutional neural networks (DCN), feed forward neural networks (including deep feed forward neural networks), recurrent neural networks (RNN) - 1521), (including historical data on wear-and-tear during usage, historical data on material deterioration under various ambient or environmental conditions - 1909).
Regarding claim 14, Cella teaches storing the first battery datum in a data storage unit (a battery management system (BMS) and other functions down to a cell level. In other examples, the smart batteries may be smart batteries with cell-level monitoring and data streams - 2426, 2427-2430) (monitoring of power system status at the individual battery level - 2292)
Regarding claim 15, Cella teaches the training step further comprises setting one or more weights and values on the one or more inputs (numeric weights that determine how much relative effect an input has on the output value of the node - 1531).
Regarding claim 16, Cella teaches retrieving information from one or more third party applications associated with the battery history associated with the first user processor (This data may produce enhanced product level data and may be combined with third party data for further processing, modeling or other adaptive or coordinated intelligence activity - 0262).
Regarding claim 17, Cella teaches generating of the predictive model is responsive to a determination that based on the first battery datum, the first battery requires one or more actions (a set of expert actions or operations upon information; process and/or workflow data; a set of models of various types; a set of outcomes – 0114).
Regarding claim 18, Cella teaches generating of the predictive model is responsive to a determination that based on one or more second data, where in the second data comprises at least weather data and power outage historical data (weather conditions - 2406) (power outages – 1910).
Regarding claim 19, Cella teaches the processor is a merchant processor associated with one or more software applications (interface with other software applications - 1229).
Regarding claim 20, Cella teaches a computer readable non-transitory medium comprising computer executable instructions that, when executed on a processor, configure the processor to perform procedures comprising the steps of (computer-readable medium in this disclosure is therefore non-transitory - 2785):
receiving, from a battery processor, a first battery datum associated with a first battery ( receive various inputs of any type, including media data such as images/video/audio data, data sets including transaction data, biometric data, motion capture data, pathology data, and/or other such data, and to analyze such data to determine further information (e.g., metadata) about the input data, objects or entities appearing in the input data, and the like – 1700);
generating a predictive model based on the battery datum, wherein the predictive model is configured to predict one or more outcomes associated with the battery (for use in generating a predictive model - 1701);
training the predictive model across one or more iterations (to train a prediction model for predicting the target variable - 1716);
updating, by the processor, the predictive model with one or more new battery datum (optimize the predictive model by updating the training data set, re-training the predictive model - 1718);
generating, by the predictive model, one or more outcomes associated with the battery ( monitor outcomes associated with classifications, predictions, and/or recommended actions to determine if the classifications and/or predictions were accurate - 1722); and
transmitting an order inquiry to a first user processor, wherein the order inquiry is transmitted in response to the one or more outcome generated by the predictive model (The recommended action and reporting circuit 9536 may transmit the reports to other modules, devices, systems, etc, as outputs – 1721, 1722).
Allowable Subject Matter
Claim 6 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
The following is an examiner’s statement of reasons for allowance:
Claim 6 is objected to because the closest prior art, Cella et al. [US 2022/0197306 A1], fails to anticipate or render obvious the merchant processor is further configured to: receive, from the battery processor, a second battery datum associated with a second battery, wherein the second battery is further associated with a second user processor; transmit a user-to-user prompt to the first user processor and the second user processor; receive, in response to the user-to-user prompt, a first prompt response from the first user processor and a second prompt response from the second user processor; transmit a user-to-user order to the first user processor and the second user processor, wherein the user-to-user order comprises at least an order to facilitate battery sharing between a first user associated with the first user processor and a second user associated with the second user processor; and receive, in response to the user-to-user order, a first order response from the first user processor and a second order response from the second user processor, in combination with all other limitations in the claim(s) as defined by applicant.
Relevant Prior Art / Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Cella et al. (US Patent Application Publication 2022/0198562 A1) discloses a system for facilitating electronic marketplace transactions;
Cella et al. (US Patent Application Publication 2022/0187847 A1) discloses a robot fleet management platform for value chain networks.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RICKY GO whose telephone number is (571)270-3340. The examiner can normally be reached on Monday through Friday from 9:00 a.m. to 5:30 p.m.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Arleen M. Vazquez can be reached on (571) 272-2619. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RICKY GO/Primary Examiner, Art Unit 2857