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 Objections
Claim 15 is objected to because of the following informalities:
The claim ends with a “;” instead of a “.”
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 1 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Relative to claim 1, it is not clear as to whether the limitation, “In a system comprising a first sorting system located at a first geographical location, a second sorting system located at a second geographical location, a data center located at a third geographical location, and a wide area network configured to enable data communications between the first sorting system and the data center, and between the second sorting system and the data center”, is included. This limitation is in the preamble.
A preamble generally is not limiting when the claim body describes a structurally complete invention such that deletion of the preamble phrase does not affect the structure or steps of the claimed invention. It appears that the claim body absent the preamble represent a structurally complete invention. See MPEP §2111.02 (II)
Applicant needs to clearly include these limitations in the body of the claim if these limitations are intended to be included.
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.
Claim(s) 1-3, 5-8, 10, 12, and 16-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Goda et al (US PG. Pub. 2022/0005171) in view of Young et al (US PG. Pub. 2022/0057345). Relative to claims 1-3, 5-8, 10 and 12, Goda discloses:
claim 1 - In a system comprising a first sorting system located at a first geographical location, a second sorting system located at a second geographical location, a data center located at a third geographical location, and a wide area network configured to enable data communications between the first sorting system and the data center, and between the second sorting system and the data center (Fig. 1), a method comprising:
classifying and sorting of a first mixture of materials by the first sorting system using a classifying/sorting algorithm (scrap materials are sorted by sorting machine 13 using learning algorithm, scrap materials sorted by the sorting machine 13 are the first mixture of materials, Para. 0036);
collecting information on results of the classifying and sorting of the first mixture of materials (Para. 0036);
transmitting the collected information from the first sorting system to the data center (15) via the network (11)(Para. 0028; 0038)(Fig. 1);
the data center (15) modifying the classifying/sorting algorithm as a result of the collected information transmitted from the first sorting system (0038);
downloading the modified classifying/sorting algorithm to the first sorting system (13) via the network (see Fig. 1, sec 108; Para. 0030);
claim 2 - downloading the modified classifying/sorting algorithm to the second sorting system (14) via the network (11)(Para. 0038); and classifying and sorting of a third mixture of materials by the second sorting system (14) using the modified classifying/sorting algorithm (materials are sorted at sorting system 14 using the updated classification; Para. 0038, 0034);
claim 3 - performing tests on the classified and sorted first mixture of materials to determine how accurate was the classification and sorting of the first mixture of materials (Para. 0037, included in the learning data to learn characteristics of each component type to improve accuracy); and
transmitting results of the tests to the data center (15) via the area network (11)(Para. 0038), the modification of the classifying/sorting algorithm is at least partially based on the results of the tests (Para. 0038);
claim 5 - the classifying/sorting algorithm is an artificial intelligence algorithm (Para. 0036);
claim 6 - the first and second sorting systems each comprise: an image capturing device configured to produce image data of the first mixture of materials (see plurality of captured images, 0029);
a conveyor system (conveyor) configured to convey the first mixture of materials past the image capturing device (Para. 0039);
a data processing system comprising an artificial intelligence system (machine learning techniques) configured to classify certain ones of the first mixture of materials based on the image data of the first mixture of materials (Para. 0028), the classifying/sorting algorithm utilizes a knowledge base containing a previously generated library of observed characteristics captured from a homogenous set of samples of the certain ones of the first mixture of materials (Para. 0037, for instance substrate is tagged using paint software by surrounding it with a red line, and the machine learning means 107 learns that the tagged component type is a substrate. The machine learning means uses the same process to learn different materials surrounded by several kinds of colors to learn or distinguish each unique component type); and
a sorter (included in sort machine 13 or 14, such as wind power sorter, or sieving machine, Para. 0045,0047) configured to sort the classified certain ones of the first mixture of materials from the first mixture of materials as a function of the classifying of certain ones of the first mixture of materials (Para. 0036);
claim 7 - the classifying and sorting of the first mixture of materials by the first sorting system (13) using the classifying/sorting algorithm comprises: producing image data of the first mixture of materials (Para. 0036);
assigning with an artificial intelligence system a classification to certain ones of the first mixture of materials based on the image data of the first mixture of materials (Para. 0036),
the classification is based on a knowledge base containing a previously generated library of observed characteristics captured from a homogenous set of samples of the certain ones of the first mixture of materials (Para. 0037); and
sorting the certain ones of the first mixture of materials from the first mixture as a function of the classification (Para.0034);
claim 8 - the classifying and sorting of the third mixture of materials by the second sorting system using the modified classifying/sorting algorithm comprises (Para. 0034): producing image data of the third mixture of materials (Para. 0039, see steps in Fig. 4, Fig. 5A-5D);
assigning with an artificial intelligence system a classification to certain ones of the third mixture of materials based on the image data of the third mixture of materials (Para. 0038), the artificial intelligence system is configured with the modified classifying/sorting algorithm (Para. 0038)(see Fig. 4); and
sorting the certain ones of the third mixture of materials from the third mixture as a function of the classification (Para. 0034)(Fig. 4, Fig. 5B);
claim 10 - the first mixture of materials comprises plastic pieces of different types of polymer compositions (Para. 0025); and
claim 12 - the data center (15) uploading the modified classifying/sorting algorithm to the network (11) for available download by the first and second sorting systems (Para. 0038)(Fig. 1).
Goda does not expressly disclose: the network is a wide area network; or
classifying and sorting of a second mixture of materials by the first sorting system using the modified classifying/sorting algorithm.
Young teaches: the network is a wide area network (see “wide area network”, end of Para. 0058); and
classifying and sorting of a second mixture of materials (energy storage devices 105, 405 are sorted) by the first sorting system (140) using the modified classifying/sorting algorithm (Para. 0063; 0066), for the purpose of providing a system and method for collecting, sorting, and packaging batteries that can efficiently sort LIBs and transmits data safely and securely between involved parties (Para. 0002; 0004; 0006).
It would have been obvious to one of ordinary skill in the art on or before the time of the filing to modify the method of Goda with the wide area network, and classifying and sorting of a second mixture of materials using the modified classifying/sorting algorithm, as taught in Young, for the purpose of providing a system and method for collecting, sorting, and packaging batteries that can efficiently sort LIBs and transmits data safely and securely between involved parties.
Relative to claims 16-17, the disclosure of Goda includes:
A system (Fig. 1) comprising:
a first sorting system (13) located at a first geographical location, the first sorting system is configured to classify and sort a first mixture of materials by using a classifying/sorting algorithm (Para. 0028), the classifying/sorting algorithm is an artificial intelligence algorithm (machine learning), the first sorting system (13) is configured to collect information on results of the classifying and sorting of the first mixture of materials (Para. 0028);
a data center (15)(0038);
a network (11) configured to enable data communications between the first sorting system (13) and the data center (15)(Para. 0038);
circuitry (included in Ref. 100) configured to transmit the collected information from the first sorting system (13) to the data center via the network (11)(Para. 0028)(Fig. 1), the data center (15) is configured to modify the classifying/sorting algorithm as a result of the collected information transmitted from the first sorting system (Para. 0038, system updates classification data)(Fig. 1); and
circuitry (included in Ref. 100) configured to communicate the modified classifying/sorting algorithm to the first sorting system (13) via the network (11)(Para. 0028)(Fig. 1),
a second sorting system (14); and
circuitry (included in Ref. 100) configured to communicate the modified classifying/sorting algorithm to the second sorting system (14) via the network (11)(Para. 0038), the second sorting system (14) is configured to classify and sort a third mixture of materials by the second sorting system (14) using the modified classifying/sorting algorithm (Para. 0034).
Goda does not expressly disclose:
the network is a wide area network;
the data center located at a second geographical location;
the second sorting system is located at a second geographical location; and
the first sorting system is configured to classify and sort a second mixture of materials by the first sorting system using the modified classifying/sorting algorithm.
Young teaches:
the network is a wide area network (Para. 0058);
the data center (see central processing server 620) is located at a second geographical location (Para. 0090, server can be remote);
the second sorting system is located at a second geographical location (see Fig. 6, multiple distinct sorting systems 610a-610c can be disposed at different locations, and each system includes a controller that is communicatively coupled to a central processing server 620, and each separate sorting system 610a-610c is similar to the system 500 shown in fig. 5A-5B); and
the first sorting system is configured to classify and sort a second mixture of materials by the first sorting system (140) using the modified classifying/sorting algorithm (sorter sorts the new batteries using updated algorithm; Para. 0063; 0066), for the purpose of providing a system and method for collecting, sorting, and packaging batteries that can efficiently sort LIBs and transmits data safely and securely between involved parties (Para. 0002; 0004; 0006).
It would have been obvious to one of ordinary skill in the art on or before the time of the filing to modify the method of Goda so that: the network is wide area network; the second sorting system located at a second geographical location; and the first sorting system classifies and sorts a second mixture of materials using the modified classifying/sorting algorithm, as taught in Young, for the purpose of providing a system and method for collecting, sorting, and packaging batteries that can efficiently sort LIBs and transmits data safely and securely between involved parties.
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Goda and Young as applied to claim 1 above, and further in view of Ji, Tian-chen (CN 113 731 836 A). Relative to claim 4, Goda in view of Young discloses all claim limitations mentioned above, including the first and second sorting systems, and the data center can be located at different geographical locations.
Goda in view of Young does not expressly disclose: the first sorting system, the second sorting system, and the data center are each located in different population centers.
Ji teaches: a solid sorting system and a data center located in a population center (system sorts waste for a city, data center is the industrial control computer 2, Page 2, Para. 6 & 8 of the English translation of the Specification, the population center is the city, which includes a system to sort solid waste for the city)(Fig. 1), for the purpose of providing a city solid waste sorting system based on deep learning that is highly efficient, improves working conditions for workers, and can operate for long periods of time (Page 1, Abstract).
It would have been obvious to one of ordinary skill in the art on or before the time of the filing to modify the system of Goda in view of Young with the city solid sorting system and data center, as taught in Ji for the purpose of providing a city solid waste sorting system based on deep learning that is highly efficient, improves working conditions, and can operate for long periods of time.
Relative to claim 4, Goda in view of Young and Ji does not expressly disclose the first sorting system, the second sorting system, and the data center are each located in different population centers.
Goda in view of Young and Ji teaches: the first sorting system, the second sorting system, and the data center are each located in different population centers, as an obvious matter of design choice based on the user’s preference.
Goda in view Young discloses multiple sorting systems (such as systems 610a-601c in Young, Fig. 6) that may be deployed in different locations and coordinated by a central server (620, Young, Para. 0090) or data center. Ji further discloses a city-wide sorting system implemented for a city, i.e, a population center.
Young also discloses that the data center (such central processing server, 620) can be remote (Para. 0090), and therefore can also be implemented at a different population center as matter of design choice by one of ordinary skill in the art.
It would have been obvious to a person of ordinary skill in the art at or before the time of the filing to implement the sorting systems of Goda in view of Young to serve different population centers, such as the city-level sorting system of Ji, and to coordinate them via the remote data center, resulting in a first sorting system, second sorting system, and data center located at different population centers, as a matter of design choice. See MPEP §2144.03.
Claim(s) 9, 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Goda in view of Young as applied to claims 9 and 16 above, and further in view of Chaganti et al (US PG. Pub. 2019/0299255). Relative to claims 9, 18, Goda in view of Young discloses all claim limitations mentioned above, but does not expressly disclose: the first mixture of materials comprises cast and wrought aluminum scrap pieces.
Changanti teaches: the first mixture of materials comprises cast and wrought aluminum scrap pieces (Para. 0019; 0022), for the purpose of providing a system and method for sorting scrap materials, including scrap materials containing metal, in a line operation that is environmentally beneficial, efficient, and provides improvements in the purity and recovery of sorted scrap materials (Para. 0001-0002; 0023; 0107).
It would have been obvious to one of ordinary skill in the art on or before the time of the filing to modify the method of Goda in view of Young with the materials comprising cast and wrought aluminum scrap pieces mentioned above, as taught in Chaganti, for the purpose of providing a system and method for sorting scrap materials, including scrap materials containing metal, in a line operation that is environmentally beneficial, efficient, and provides improvements in the purity and recovery of sorted scrap materials.
Claim(s) 11 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Goda in view of Young as applied to claims 1 and above, and further in view of Spencer et al (US PG. Pub. 2013/0079918). Relative to claims 11 and 19, Goda in view of Young discloses all claim limitations mentioned above, but does not expressly disclose:
the classifying/sorting algorithm is configured for classifying and sorting of Zorba using a combination of one or more vision systems and one or more XRF systems implemented within the first sorting system; or
the classifying/sorting algorithm is configured for classifying and sorting of Zorba using a combination of one or more vision systems and one or more XRF systems implemented within the first sorting system.
Spencer teaches: the classifying/sorting algorithm is configured for classifying and sorting of Zorba using a combination of one or more vision systems and one or more XRF systems implemented within the first sorting system (Para. 0060; 0096), for the purpose of providing a materials sortation apparatus and method that is capable of efficiently sorting small individual machining chips, in a cost effective manner (Para. 0003; 0008).
It would have been obvious to one of ordinary skill in the art on or before the time of the filing to modify the system of Goda in view of Young with the classifying and sorting of Zorba using a combination of one or more vision systems and one or more XRF systems, as taught in Spencer, for the purpose of providing a materials sortation apparatus and method that is capable of efficiently sorting small individual machining chips, in a cost effective manner.
Claim(s) 13-14 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Goda in view of Young as applied to claim 1 above, and further in view of Petra (WO 2021/0179037 A1). Relative to claims 13-14, and 20 Goda in view of Young discloses all claim limitations mentioned above, but does not expressly disclose:
the data center associating a sustainability-related score to the classified and sorted second mixture of materials, the sustainability-related score is based on the information collected from the first sorting system; or
the sustainability-related score is selected from the group consisting of carbon tax credits, carbon reduction values, verified carbon units ("VCUs"), and greenhouse gas ("GHG") emission reductions.
Petra teaches: the data center (control and management system, 107, Para. 0127)(Fig. 1) associating a sustainability-related score to the classified and sorted second mixture of materials, the sustainability-related score is based on the information collected from the first sorting system (Para. 0399; 0397); and
the sustainability-related score is selected from the group consisting of carbon tax credits, carbon reduction values, verified carbon units ("VCUs"), and greenhouse gas ("GHG") emission reductions (Para. 0392; 0403), and
the data center (107)(Fig. 1) is configured to assign a sustainability-related score to the classified and sorted second mixture of materials, the sustainability-related score is based on the information collected from the first sorting system (Para. 0399; 0397),
the sustainability-related score is selected from the group consisting of carbon tax credits, carbon reduction values, verified carbon units ("VCUs"), and greenhouse gas ("GHG") emission reductions (Para. 0397; 0403).
Petra teaches: the data center associates a sustainability-related score to the classified and sorted second mixture of materials, the sustainability-related score is selected from the group consisting of carbon tax credits, carbon reduction values, verified carbon units ("VCUs"), and greenhouse gas ("GHG") emission reductions as described above, for the purpose of providing an improved method for producing a thermal product with a consistent and designable thermal property and can dynamically process waste to remove unwanted materials (Para. 0009; 0101).
It would have been obvious to one of ordinary skill in the art on or before the time of the filing to modify the method of Goda in view of Young so that the data center associates a sustainability-related score to the classified and sorted second mixture of materials, the sustainability-related score is selected from the group consisting of carbon tax credits, carbon reduction values, verified carbon units ("VCUs"), and greenhouse gas ("GHG") emission reductions described above, as taught in Petra for the purpose of an improved method for producing a thermal product with a consistent and designable thermal property and can dynamically process waste to remove unwanted materials.
Claim(s) 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Goda in view of Young as applied to claim 1 above, and further in view of Eilbert et al (US PG. Pub. 2010/0290691). Relative to claim 15, Goda in view of Young discloses all claim limitations mentioned above, but does not expressly disclose:
the system further comprises a point-of-use analyzer located at a fourth geographical location, the point-of-use analyzer is configured to analyze a sample of materials taken from a shipping container to confirm the compositions of the materials contained within the shipping container, and
further transmitting the analysis of the shipping container materials from the point-of-use analyzer to the data center via the wide area network.
Eilbert teaches: the system further comprises a point-of-use analyzer (100, including Ref. 120)(Fig. 1) located at a fourth geographical location (Para. 0060), the point-of-use analyzer (100, 120) is configured to analyze a sample of materials taken from a shipping container to confirm the compositions of the materials contained within the shipping container (Para. 0055, system analyzes inspection volume 105 which may be a shipping container, cargo container of a truck, or railcar, Para. 0067; the system may be located at a fourth location, such as checkpoint or channel), and
further transmitting the analysis of the shipping container materials from the point-of-use analyzer (100, 120) to the data center (340)(Fig. 3) via a network (input/output interface 354, 326; Para. 0066)(Fig. 3), for the purpose of providing a method for the automated detection of objects using material discrimination for large containers such as trucks, shipping containers, rail cars, and containerized cargo to accurately characterize an item of interest (Para. 0033).
It would have been obvious to one of ordinary skill in the art on or before the time of the filing to modify the method of Goda in view of Young with the point-of-use analyzer located at a fourth geographical location, and further transmitting the analysis of the shipping container materials from the point-of-use analyzer to the data center mentioned above, as taught in Eilbert, for the purpose of providing a method for the automated detection of objects using material discrimination for large containers to accurately characterize an item of interest.
Relative to claim 15, the disclosure of Goda in view of Young and Eilbert does not expressly disclose that the network for transmitting the analysis of the shipping container materials from the point-of-use analyzer to the data center is a wide area network.
Goda in view of Young and Eilbert can be modified so that network for transmitting the analysis of the shipping container materials from the point-of-use analyzer to the data center is a wide area network as a matter of design choice.
Young discloses transmitting analyzed sortation results of the sorting process of a sorting system using machine learning, and the results are transmitted from the sorting system to a data center (620) using a wide area network (Para. 0057-0058).
It would have been obvious to one of ordinary skill in the art at or before the time of the filing, to implement the wide area network for transmitting the analysis results as disclosed in Young, to the method of Goda in view of Young and Eilbert so that the analysis of the shipping container materials is transmitted from the point-of-use analyzer to the data center via a wide area network, as a matter of design choice, since wide area networks are well known in the art of communication systems. See MPEP §2144.03
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to YOLANDA RENEE CUMBESS whose telephone number is (571)270-5527. The examiner can normally be reached M-F 10-6.
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, Gene Crawford can be reached at 571-272-6911. 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.
/YOLANDA R CUMBESS/Primary Examiner, Art Unit 3651
Prosecution Insights