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 .
1. Claims 1-20 are presented for examination.
Claim Rejections - 35 USC § 101
2. 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 1-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1, 8 and 15 recites constructing a knowledge corpus using data received from a plurality of sources regarding electrical power harvesting from smart materials; determining one or more objects to be utilized for generating electrical power, wherein the one or more objects are comprised of at least one or more smart materials; generating printing instructions for the one or more objects to be executed by a three-dimensional printer; and monitoring a performance of the one or more objects within an environment.
The limitation of “determining” … and “monitoring” as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind (performed in the human mind observations, evaluations, judgments, and opinions, are considered to recite an abstract idea), but for the recitation of generic computer components, as claims 8 and 15. that is, other
than reciting “by a processor,” nothing in the claim element precludes the step from practically
being performed in the mind. For example, but for the “by a processor” language, “determining” and “monitoring” in the context of this claim encompasses the user manually can observe, evaluate and judge. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The judicial exception is not integrated into a practical application. In particular, the claim 1 recites additional element – “constructing a knowledge corpus from the received data…”, as claim 1 no addition tool use to “constructing a knowledge corpus”, as claim 8 and 15, the computer is used as a tool to perform the generic computer function of “constructing a knowledge corpus”, are mere data gathering recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05. In addition, the claims recite addition element – “print the object in 3D printer” also merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element “print the object in 3D printer” limits the identified judicial exceptions, this additional element(s) does no more than generally link the use of the judicial exception to a particular technological environment or field of use. This type of limitation merely confines the use of the abstract idea to a particular technological environment (printer) and thus, fails to add an inventive concept to the claims. See MPEP 2106.05(h). Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application and the claim is directed to the judicial exception.
The claims do not include additional elements that are sufficient to amount to significantly more
than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, receiving a plurality of data from a user and constructing a knowledge corpus, (insignificant extra- solution elements - mere data gathering, see MPEP 2106.05 I A), using a processor (merely applying the exception with a generic computer - see MPEP 2106.04(a)(2) III C). In addition, the addition element “print the object in 3D printer” simply (linking the use of the judicial exception to a particular technological environment or field of use (printer) . Also note that printing object with 3D printer are well-understood routine and conventional, see references cited below in the rejection under 35 U.S.C. § 103 In addition, for example, Cui et al., “ Three-dimensional printing of piezoelectric materials”; Shi et al., “Extrusion 3D printing of Piezoelectric”, and Pugliese et al. AI empower 3D and 4D printing … smart Biomedical Material. (cited prior art in IDS and PTO-892). Therefore, considering the additionally elements individually and in combination and the claim as a whole, the additional elements do not provide significantly more than the abstract idea.
Claims 2, 9 and 16, recites analyzing the data received from the plurality of sources using one or more machine learning models (“Mental Processes” grouping of abstract ideas); and generating insights for a plurality of smart materials and storing the insights in the knowledge corpus (amounts to no more than insignificant pre-activity of receiving data). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Claims 3, 10 and 17, recites retraining one or more machine learning models based on the performance of the one or more objects within the environment (applying a machine learning is merely applying the exception with a computer - see MPEP 2106.04(a)(2) III C). Thus, this claim recites an abstract idea.
Claim 4, 11 and 18, recites determining the one or more objects to be utilized for generating the electrical power (mental process) further comprises: simulating a performance of a plurality of candidate objects under a plurality of conditions of the environment using one or more forecasting machine learning models (insignificant application, i.e. extra-solution activity, see 2106.05(g)). Thus, this claim recites an abstract idea.
Claim 5, 12 and 19, recites the plurality of conditions of the environment are determined based on insights derived by one or more machine learning models (mental process)and natural language processing using the data received from the plurality of sources and environmental data provided by a user in a sustainable energy user interface (applying the abstract idea with a natural language processor (merely applying the exception with a computer - see MPEP 2106.04(a)(2) III C). Note that natural language processors and machine learning are well-understood routine and conventional, see references cited below in the rejection under 35 U.S.C. § 103. Thus, this claim recites an abstract idea.
Claims 6, 13 and 20, recites ranking a plurality of candidate objects based on the simulated performance using a machine learning based recommendation system (user thinking to rank based on the performance, it falls within the “Mental Processes” grouping of abstract ideas); displaying the ranking of the plurality of candidate objects to a user within a sustainable energy user interface (insignificant extra-solution activity see MPEP 2106.04(a)(2)IIIA regarding displaying information); and receiving one or more selections from the plurality of candidate objects from the user (insignificant extra-solution activity - mere data receiving the transmitting, see MPEP 2106.05.1 A); wherein the one or more selections received from the user are the one or more objects to be utilized for generating the electrical power (intended use, See also MPEP § 2112 - MPEP § 2112.02). The claim is directed to an abstract idea.
claims 7, 14 and 21, recite re-rank future candidate objects based on the one or more selections or feedback provided by the user (user thinking to re-rank based on the performance, it falls within the “Mental Processes” grouping of abstract ideas). The claim is directed to an abstract idea.
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.
3. 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.
3.1 Claim(s) 1, 8 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gil (KR 2019-0076377 A) in view of Islam et al. (Boosting Piezoelectricity by 3D Printing PVDF-MoS2 Composite as a Conformal and High-Sensitivity Piezoelectric Sensor).
Regarding claims 1, 8 and 15, Gil discloses a method/computer system and computer program product for sustainable power harvesting (page 7, par. 7, a sustainable power source for consumer electronic devices), method/computer system and computer program product comprising:
program instructions (Abstract, a data processing and application layer processing and storing a large-scale data set, which is transferred from the data preprocessing layer, in a distributed shape), stored on at least one of the one or more computer-readable storage media (page 14, par. 3, stored in the personal server) for execution by at least one of the one or more processors (page 14, Par. 3, Data generated with the aid of the microcontroller or using preprocessor) via at least one of the one or more memories (page 6, par. 2, the data processing and application layer (or data processing and application unit) is responsible for overall processing and decision making and includes a queue unit, a Hadoop server, a storage, and an event management unit);
constructing a knowledge corpus using data received from a plurality of sources regarding electrical power (Abstract, an energy harvesting system based on the internet of things and big data analytics, an energy harvesting and data generation layer using a harvester attached to a pressure region of a human body, supplying power to a health monitoring sensor, and obtaining health information from the health monitoring sensor)and harvesting from smart materials (page 6, par. 3, harvesting energy using a piezoelectric device);
determining one or more objects to be utilized for generating electrical power (page 5, Par. 8, the harvester is made of a piezoelectric device and converts the pressure of the human body into electric energy), wherein the one or more objects are comprised of at least one or more smart materials (Page 6, par. 3, harvesting energy using a piezoelectric device); and
monitoring a performance of the one or more objects within an environment (Page 7, par. 12-14, page 8, par. 3, powering the IoT terminal nodes used for health monitoring, data processing, intelligent decision making and event management).
Gil fails to discloses generating printing instructions for the one or more objects to be executed by a three-dimensional (3D) printer.
However, Islam discloses generating printing instructions for the one or more objects to be executed by a three-dimensional (3D) printer (Title, Abstract, Boosting Piezoelectricity by 3D Printing PVDF-MoS2 Composite as a Conformal and High-Sensitivity Piezoelectric Sensor ) discloses 3D printing print a piezoelectricity in polyvinylidene fluoride (PVDF). Designing and fabricating PVDF-MoS 2 based piezoelectric sensors as well as predicting and engineering their piezoelectric properties for sensing, actuating for energy harvesting applications).
Gil and Islam are analogous art. They relate to energy harvesting. Therefore, before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify printing energy harvesting material, taught by Islam, incorporated with energy harvesting system based on the internet of things and big data analytics, taught by Gil, in order to provide highly desirable for sensing and energy harvesting of 3D conformal structures.
3.2 Claim(s) 2-4, 9-11 and 16-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gil (KR 2019-0076377 A) in view of Islam et al. (Boosting Piezoelectricity by 3D Printing PVDF-MoS2 Composite as a Conformal and High-Sensitivity Piezoelectric Sensor) further in view of Castillo et al. (Machine Learning Identification of Piezoelectric Properties).
Regarding claims 2, 9 and 16, the combination of Gil and Islam disclose the limitations of claims 1, 8 and 15. In addition, Gil discloses generating insights for a plurality of smart materials and storing the insights in the knowledge corpus (page 4, par. 8-13, page 15, par. 4 and 9-10, using a piezoelectric sensor in a health monitoring sensor, planning using big data analysis to implement real-time data processing and decision making in the medical system and perform data processing, intelligent decision making and self-contained data collection, the storage device is intended to store processed results, which are later used to make decisions), but the combination of Gil and Islam fail to disclose constructing the knowledge corpus further comprises: analyzing the data received from the plurality of sources using one or more machine learning models. However, Castillo discloses the limitation of claims 2, 9, 16 as follow: analyzing the data received from the plurality of sources using one or more machine learning models (abstract, a machine learning approach to determine the parameters in the model).
Castillo, Gil and Islam are analogous art. They relate to energy harvesting. Therefore, before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify machine learning approach to determine the parameters in the model, taught by Castillo, incorporated with teaching of Gil and Islam, as state above, since Neural network is ready to use and quickly solving the problem using the impedance curve as input and the parameter of the output.
Regarding claims 3, 10 and 17, the combination of Gil and Islam disclose the limitations of claims 1, 8 and 15, but fail to disclose the limitations of claims 3, 10 and 17. However, Castillo discloses the limitation of claims 3, 10, 17 as follow: Castillo discloses retraining one or more machine learning models based on the performance of the one or more objects within the environment (page 6, section 3, Fig. 2, Fig. 3, page 14, section 3.3, raining a NN is an optimization process where, given a proposed model and some data, the objective is to find the parameters of the model that better fit the data).
Castillo, Gil and Islam are analogous art. They relate to energy harvesting. Therefore, before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify machine learning approach to determine the parameters in the model, taught by Castillo, incorporated with teaching of Gil and Islam, as state above, since Neural network is ready to use and quickly solving the problem using the impedance curve as input and the parameter of the output.
Regarding claims 4, 11 and 18, the combination of Gil and Islam disclose the limitations of claims 1, 8 and 15. In addition, Islam discloses in the Abstract, impacted the increase in the composite piezoelectric coefficient which was further sup-ported by the simulation results), but the combination of Gil and Islam fail to disclose simulating a performance of a plurality of candidate objects under a plurality of conditions of the environment using one or more forecasting machine learning models. However, Castillo discloses the limitation of claims 4, 11 and 18, as follow: simulating a performance of a plurality of candidate objects under a plurality of conditions of the environment using one or more forecasting machine learning models (page 7, section 3.3 and 3.4 uses the simulations to train the NN, and then, the outcome of the process is a complex function that given the impedance curve, returns the proposed parameters; simulations are used in the training of the NN and in the evaluation of the performance).
Castillo, Gil and Islam are analogous art. They relate to energy harvesting. Therefore, before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify machine learning approach to determine the parameters in the model, taught by Castillo, incorporated with teaching of Gil and Islam, as state above, since Neural network is ready to use and quickly solving the problem using the impedance curve as input and the parameter of the output.
3.3 Claim(s) 5, 12 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gil (KR 2019-0076377 A) in view of Islam et al. (Boosting Piezoelectricity by 3D Printing PVDF-MoS2 Composite as a Conformal and High-Sensitivity Piezoelectric Sensor) further in view of Castillo et al. (Machine Learning Identification of Piezoelectric Properties) furthermore in view of MIRESMAILLI (US 2019/0170718 A1).
Regarding claim 5, 12 and 19, the combination of Gil, Islam and Castillo disclose the limitations of claims 1, 4, 8, 11, 15 and 18, but fail to disclose the limitations of claims 5, 12 and 19. However, Miresmailli discloses the limitation of claims 5, 12, 19 as follow:
Regarding claims 5, 12 and 19, Miresmailli discloses the plurality of conditions of the environment are determined based on insights derived by one or more machine learning models ([0121], Learning from Correlation of Future Crop Performance with Past Data and natural language processing ([0126] Natural Language Processing (NLP) using the data received from the plurality of sources and environmental data provided by a user in a sustainable energy user interface ([0126], [0148], [0081] DPU 540 stores sensor data and information that it receives in one or more databases 545. It also performs data correlation (correlating an assessment of the health of individual plants as inputted by the expert with sensor data captured for the same plants) and stores the resultant “trained data” in one or more databases 545).
Miresmailli , Castillo, Gil and Islam are analogous art. They relate to energy harvesting. Therefore, before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify trained based on correlating an assessment, taught by Miresmailli, incorporated with teaching of Castillo, Gil and Islam, as state above, to provide a normalizing, and filtering high-capacity health information data obtained from the harvesting and data generation layer.
Allowable Subject Matter
4. Claims 6, 13 and 20 would be allowable if rewritten to overcome the rejection(s) under 101 set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
As claims 7, 14, 21 are directly or indirectly dependent on claims 6, 13 and 20, those claims are also allowable at least by virtue of their dependency, if rewritten to overcome the rejection(s) under 101 set.
Citation Pertinent prior art
5. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Cui et al. (Three-dimensional printing of piezoelectric materials with designed anisotropy and directional response) discloses coupling anisotropy and orientation effects, producing them via additive manufacturing (3D printing) of highly responsive piezo electric materials. This creates the freedom to inversely design an arbitrary piezoelectric tensor, including symmetry conforming and breaking properties, transcending the common coupling modes observed in piezoelectric monolithic and foams.
Valentino (US20140299783A1) discloses an apparatus comprising: a radiation sensor having one or more radiation sensors and an on-board power harvester.
Allen (US 2015/0356203 A1) discloses the plurality of sources of content in the corpus of information associated with the domain.
Priya et al. (US20080174273A) discloses the mechanism comprises a plurality of elongated piezoelectric elements for generating electric energy from mechanical energy, piezoelectric Energy Harvester.
Daviona, (Smart Materials and Devices for Energy Harvesting ) discloses Energy harvesters based on magneto-strictive alloys are intrinsically robust and long life. Indeed, these materials inherit most of the mechanical properties of iron, which is the main component of the alloy. The devices make use of kinetic energy and by having no moving parts, are robust and simple because the energy conversion takes place within the material.
A reference to specific paragraphs, columns, pages, or figures in a cited prior art reference is not limited to preferred embodiments or any specific examples. It is well settled that a prior art reference, in its entirety, must be considered for allthat it expressly teaches and fairly suggests to one having ordinary skill in the art. Stated differently, a prior art disclosure reading on a limitation of Applicant's claim cannot be ignored on the ground that other embodiments disclosed wereinstead cited. Therefore, the Examiner's citation to a specific portion of a single prior art reference is not intended to exclusively dictate, but rather, to demonstrate an exemplary disclosure commensurate with the specific limitations being addressed. In re Heck, 699 F.2d 1331, 1332-33,216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1 009, 158 USPQ 275, 277 (CCPA 1968)). In re: Upsher-Smith Labs. v. Pamlab, LLC, 412 F.3d 1319, 1323, 75 USPQ2d 1213, 1215 (Fed. Cir. 2005); In re Fritch, 972 F.2d 1260, 1264, 23 USPQ2d 1780, 1782 (Fed. Cir. 1992); Merck& Co. v. Biocraft Labs., Inc., 874 F.2d804, 807, 10 USPQ2d 1843, 1846 (Fed. Cir. 1989); In re Fracalossi, 681 F.2d 792,794 n.1, 215 USPQ 569, 570 n.1 (CCPA 1982); In re Lamberti, 545 F.2d 747, 750, 192 USPQ 278, 280 (CCPA 1976); In re Bozek, 416 F.2d 1385, 1390, 163USPQ 545, 549 (CCPA 1969).
Conclusion
6. Any inquiry concerning this communication or earlier communications from the examiner should be directed Kidest Worku whose telephone number is 571-272-3737. If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Ali Mohammad can be reached on 571-272-4105. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KIDEST WORKU/Primary Examiner, Art Unit 2119
Prosecution Insights