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
Claims 1 and 8 are objected to because of the following informalities: a. Claim 1, line 5 recites “with a device”. It should be “with the device”. Claims 9 and 15 have the same issue.
b. Claim 8, lines 1-2 recite “selecting a machine learning model”. It should be “selecting the machine learning model”. 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.
Claims 5, 6, 13, 14, 19, and 20 are 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.
Claims 5, 13, and 19 recite the limitation "the location of the device" in lines 3 and/or 4. There is insufficient antecedent basis for this limitation in the claims.
Referring to claims 6, 14, and 20, the last line recites, “and error correcting code being used by the device”. This is not clear because claim 1 also recites “a trained ML model to determine an error correcting code to be used for the device”. Are the two instances of “error correcting code” referring to the same error correcting code? Or different error correcting codes?
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 15-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claimed "memory" (claim 15, line 2) can be reasonably interpreted to include both transitory and non-transitory embodiments. Transitory embodiments are not directed to statutory subject matter. See In re Nuijten, 500 F.3d 1346, 1356-57 (Fed. Cir. 2007).
A claim drawn to such a memory that covers both transitory and non-transitory embodiments may be amended to narrow the claim to cover only statutory embodiments to avoid a rejection under 35 U.S.C. § 101 by adding the limitation "non-transitory" to the claim.
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.
Claims 1-3, 5-11, 13-17, 19, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over US Patent Publication 2019/0181937 to Nammi (herein Nammi) in view of US Patent 11,342,937 to Gabrys et al (herein Gabrys).
Referring to claim 1, Nammi discloses a computer-implemented method (Figure 10), comprising: receiving a plurality of data associated with a device, wherein the plurality of data includes location data associated with the device; receiving environmental data; applying the plurality of data associated with a device and the environmental data to determine an error correcting code to be used for the device based on an output of the ML model (Figure 8, elements 810, 830, & 840 and paragraph [0054]); and sending the error correcting code to the device (Figure 3 and paragraph [0032]). Nammi does not explicitly disclose “as an input to a trained Machine Learning (ML) model”. However, in an analogous art directed to advanced methods of dynamic selection of ECCs in communication systems, Gabrys discloses trained Machine Learning models (see column 2, lines 29-37). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the location-based ECC selection based on modeling of Nammi with the use of trained Machine Learning (ML) models of Gabrys, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of the ordinary skill in the art would have recognized that the results of this combination would provide a predictable result of a system able to adapt selection of ECCs more dynamically and flexibly.
Referring to claim 2, Nammi in view of Gabrys discloses wherein the error correcting code is forward error correction (FEC) (Nammi, paragraph [0054]).
Referring to claim 3, Nammi in view of Gabrys further comprising determining a percentage of redundancy as a parameter of the determined error correcting code (Gabrys, column 6, lines 54-60).
Referring to claim 5, Nammi in view of Gabrys discloses wherein the environmental data includes proximity/direction to signal source (Nammi, Figure 8, element 840).
Referring to claim 6, Nammi in view of Gabrys discloses wherein the plurality of data includes time data associated with the device (Nammi, Figure 3 and paragraph [0033]).
Referring to claim 7, Nammi in view of Gabrys discloses transmitting a signal to the device and to a source device, wherein the signal causes the device and the source device to utilize the determined error correcting code during communication; and causing the device and the source device to change a percentage of redundancy to be used in association with the determined error correcting code (Gabrys, column 2, lines 29-37 & column 6, lines 54-60).
Referring to claim 8, Nammi in view of Gabrys discloses further comprising selecting a machine learning model (ML) from a plurality of ML models based on the location data (Nammi, paragraph [0035], “FEC selection model(s)”).
Referring to claims 9-11, 13-17, and 19-20, the claims recite similar limitations as found in rejected claims 1-3 and 5-8, therefore, are similarly rejected as unpatentable over Nammi in view of Gabrys.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-17 of U.S. Patent No. 12,278,651 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of this application are encompassed within and, therefore, anticipated or obvious variants of the claims of the ‘651. Claims 1-8 of this application are mapped to the corresponding claims of the ‘651 as an example.
Claims Mapping
Claim #
This Application
Claim #
US 12,278,651 B2
1
A computer-implemented method, comprising:
1
A computer-implemented method, comprising
1
receiving a plurality of data associated with a device, wherein the plurality of data includes location data associated with the device;
1
receiving a plurality of data associated with a device, wherein the plurality of data includes a location data associated with the device;
1
receiving environmental data;
4
receiving environmental data
1
applying the plurality of data associated with a device and the environmental data as an input to a trained Machine Learning (ML) model to determine an error correcting code to be used for the device based on an output of the ML model;
14
applying the plurality of data as an input to a trained Machine Learning (ML) model to determine an error correcting code to be used for the device based on an output of the ML model;wherein the applying and the determining is further based on the environment data.
1
sending the error correcting code to the device.
1
sending the error correcting code to the device;
2
wherein the error correcting code is forward error correction (FEC)
2
wherein the error correcting code is forward error correction (FEC)
3
comprising determining a percentage of redundancy as a parameter of the determined error correcting code
3
comprising determining a percentage of redundancy as a parameter of the determined error correcting code
4
determining a transcoding to be used for the device as a parameter of the determined error correcting code; and transmitting a signal to a source device to change an already in use transcoding to the determined transcoding
1
determining a transcoding to be used for the device as a parameter of the determined error correcting code; and transmitting a signal to a source device to change an already in use transcoding to the determined transcoding
5
wherein the environmental data includes one or more of density of users within a given geographical location, load or congestion of a network, connectivity type, network type, weather, signal fade associated with the location of the device, and proximity/direction to signal source
4
receiving environmental data that includes one or more of density of users within a given geographical location, load or congestion of a network, connectivity type, network type, weather, signal fade associated with the location of the device, and proximity/direction to signal source
6
wherein the plurality of data includes one or more of device type data, time data associated with the device, speed of travel associated with the device, acceleration associated with the device, and error correcting code being used by the device
5
wherein the plurality of data includes one or more of device type data, time data associated with the device, speed of travel associated with the device, acceleration associated with the device, and error correcting code being used by the device
7
transmitting a signal to the device and to a source device, wherein the signal causes the device and the source device to utilize the determined error correcting code during communication; and causing the device and the source device to change a percentage of redundancy to be used in association with the determined error correcting code
6
transmitting a signal to the device and to a source device, wherein the signal causes the device and the source device to utilize the determined error correcting code during communication; and causing the device and the source device to change a percentage of redundancy to be used in association with the determined error correcting code
8
selecting a machine learning model (ML) from a plurality of ML models based on the location data.
7
selecting a machine learning model (ML) from a plurality of ML models based on the location data
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Justin Knapp whose telephone number is (571)270-3008. The examiner can normally be reached 8:00 am - 4:30 pm (ET).
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, Albert Decady can be reached at (571) 272-3819. 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.
Justin R. Knapp
Primary Examiner
Art Unit 2112
/JUSTIN R KNAPP/Primary Examiner, Art Unit 2112
Prosecution Insights