DETAILED ACTION
Response to Arguments
The amendment filed 4/22/2026 have been entered and made of record.
The Applicant has canceled claim(s) 11.
The application has pending claim(s) 1-2, 4, 6-8, 10, and 12-20 (withdrawn claims 16-20 are withdrawn from further consideration).
In response to the amendments filed on 4/22/2026:
The objections to the claims have been entered, but the Applicant has not amended a few of the addressed claim objections and therefore the Examiner has once again addressed these issues.
The claim rejections under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph have been entered and therefore the Examiner withdraws the rejections under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph.
The claim rejections under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph have been entered and therefore the Examiner withdraws the rejections under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph.
The Applicant's arguments with respect to claims 1-2, 4, 6-8, 10, and 12-15 have been considered but are moot in view of the new ground(s) of rejection at least because the Applicant has amended independent claim(s) 1 respectively.
Applicant’s arguments, see “Claim Rejections Under 35 U.S.C. 103 …” in pages 7-8, filed 4/22/2026, with respect to the rejection(s) of claim(s) 1-2, 4, 6-8, 10, and 12-15 under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in further view of the newly found prior art reference Kamata (US 2020/0311059 A1). Further discussions are addressed in the prior art rejection section below. Therefore claims 1-2, 4, 6-8, 10, and 12-15 are still not in condition for allowance because they are still not patentably distinguishable over the prior art reference(s).
Further, the Applicant is invited to initiate a telephonic interview with the Examiner to discuss possible further amendments toward current pending independent claim 1 that might overcome the prior art of record.
Claim Objections
Claims 1, 8, and 13-14 are objected to because of the following informalities:
Claim 1 at line 14: “associate with” should still be -- associated with --.
Claim 8 at line 3; and claim 13 at line 2; and claim 14 at lines 2-3 respectively: Due to the current amendments, “the one or more image frames” should be -- the plurality of image frames --.
Appropriate correction is required.
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) 1-2 ,4, 8, and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Paul et al (US 2012/0155712 A1, as applied in previous Office Action) in view of Kamata (US 2020/0311059 A1).
Re Claim 1: Paul discloses an image processing system (see Paul, [0051], [0053]) comprising: an image frame capture device configured to extract a plurality of image frames from a video signal (see Paul, [0022], capturing and producing image(s) from a digital video recording camera); a license plate number (LPN) recognition module configured to generate a plurality of image frames, wherein the plurality of image frames include an alphanumeric character string representative of a license plate number (see Paul, [0007], [0021]-[0023], generate the license plate image by segmenting the captured image, for one or more image(s), to isolate the license plate which includes an alphanumeric character string representation of a license plate number, [0051], [0053], computer processor implemented); and a vehicle recognition module, configured to identify one or more vehicle characteristics from the plurality of image frames and associate the one or more vehicle characteristics with one or more alphanumeric descriptors, wherein the vehicle recognition module is further configured to generate a confidence level value associated with each of the alphanumeric characters in the alphanumeric character string, by extracting and identifying data corresponding to each alphanumeric character in the alphanumeric character string from the license plate number in the image frame (see Paul, [0007], [0021]-[0024], [0026]-[0027], [0030], auxiliary data [associated with e.g. vehicle specific image characteristic data: vehicle color, shape, design elements near the license plate, etc. determined by various image processing algorithms] is also determined and used in determining a correct vehicle license plate number, wherein the characters are further isolated from the license plate image and determine an associated confidence level for each recognized character [e.g. return a single possible match for each of the first five characters and return two possible matches, “B” or “3”, for the sixth character, wherein each character having an associated confidence], [0051], [0053], computer processor implemented).
However Paul fails to explicitly disclose where Kamata discloses generate a confidence level value associated with each of the alphanumeric characters in the alphanumeric character string, by extracting and identifying pieces of metadata corresponding to each alphanumeric character in the alphanumeric character string from text in the image frame, wherein a confidence level value is determined for each piece of metadata based on confidence levels associate with each piece of metadata for a current image frame and a prior image frame and selecting the piece of metadata with the highest confidence level value for each character in the character string (see Kamata, Fig. 3B, [0038], metadata associated with the character, the recognized character with the top high confidence selection).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Paul’s system using Kamata’s teachings by including the metadata associated with character based confidence level processing to Paul’s license plate character confidence level processing in order to improve the character recognition of text based images with top high confidence (see Kamata, Fig. 3B, [0038]).
Re Claim 2: Paul further discloses wherein the vehicle recognition module comprises a feature recognition and classification engine, wherein the feature recognition and classification engine are configured to use machine learning techniques implemented by a neural network (see Paul, [0007], [0021]-[0023], neural network implemented).
Re Claim 4: Paul further discloses a database of predetermined alphanumeric descriptors, and wherein the vehicle recognition module is configured to obtain the one or more alphanumeric descriptors from the database of predetermined descriptors (see Paul, [0007], [0021]-[0023], [0026]-[0027], [0030]-[0031], [0034], entries in a database wherein auxiliary data is also used in determining a correct vehicle license plate number, [0051], [0053], computer processor implemented).
Re Claim 8: Paul as modified by Kamata further discloses wherein the vehicle recognition module is further configured to attach as metadata the one or more alphanumeric descriptors to the one or more image frames (see Paul, [0007], [0021]-[0023], [0026]-[0027], [0030]-[0031], [0034], auxiliary data and vehicle data, wherein the image is also stored with the auxiliary data [0051], [0053], computer processor implemented).
Re Claim 14: Paul further discloses the LPN recognition module is configured to generate the alphanumeric character string from the one or more image frames using an optical character recognition (OCR) engine (see Paul, [0007], [0021]-[0023], using OCR, [0051], [0053], computer processor implemented).
Claim(s) 6-7, 10, 12-13, and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Paul as modified by Kamata, and further in view of Istenes et al (US 2018/0107892 A1, as applied in previous Office Action). The teachings of Paul as modified by Kamata have been discussed above.
Re Claim 6: Although Paul further discloses wherein the vehicle recognition module is further configured to generate an overall confidence value, by combining the confidence level values for each alphanumeric character in the alphanumeric character string to derive the overall confidence value, indicative of an overall confidence value that the one or more vehicle characters have been correctly identified (see Paul, [0007], [0021]-[0023], [0025]-[0027], [0030]-[0031], confidence numbers for each character in the license plate image are averaged together to determine the associated confidence level for that license plate, wherein auxiliary data is also determined and used in determining a correct vehicle license plate number, [0051], [0053], computer processor implemented), Paul [as modified by Kamata] however fails to explicitly disclose where Istenes discloses generate an overall confidence value indicative of an overall probability that the one or more vehicle characters have been correctly identified (see Istenes, [0037]-[0039], a confidence level is a percentage that defines the likelihood of being correct).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify Paul’s system, as modified by Kamata, using Istenes’s teachings by including the probability indicative confidence level to Paul’s [as modified by Kamata] confidence level in order to improve the recognition of the license plate number of the vehicle (see Istenes, [0037]-[0039]).
Re Claim 7: Paul as modified by Kamata and Istenes further discloses wherein the vehicle recognition module is configured to generate the overall confidence value via a weighted average (see Paul, [0007], [0021]-[0023], [0025]-[0027], [0030]-[0031], confidence numbers for each character in the license plate image are averaged together to determine the associated confidence level for that license plate, [0051], [0053], computer processor implemented).
Re Claim 10: As to claim 10, the discussions are addressed with regard to claim 6 respectively. Further, Paul as modified by Kamata and Istenes further discloses a compiler engine configured to generate a probability value indicative of an overall confidence value that a vehicle has been identified by the image processing system (see Paul, [0007], [0021]-[0023], [0026]-[0027], [0030], [0034]-[0035], confidence numbers for each character in the license plate image are averaged together to determine the associated confidence level for that license plate, updated confidence level for each auxiliary data, [0051], [0053], computer processor implemented) (see Istenes, [0037]-[0039], a confidence level is a percentage that defines the likelihood of each character being correct). See claim 6 for obviousness and motivation statements.
Re Claim 12: Paul further discloses at a vehicle profile database, wherein the vehicle profile database includes a plurality of previously saved vehicle profiles, each including at least one previous image data file with an associated license plate number and associated alphanumeric descriptors (see Paul, [0007], [0021]-[0023], [0026]-[0027], [0030]-[0031], [0034], entries in a database wherein auxiliary data is also used in determining a correct vehicle license plate number, wherein the image is also stored with the auxiliary data, [0051], [0053], computer processor implemented).
Re Claim 13: Paul further discloses wherein the compiler engine is configured to match the one or more image frames to one of the vehicle profiles using at least one of the one or more alphanumeric descriptors (see Paul, [0007], [0021]-[0023], [0026]-[0027], [0030]-[0031], [0034], entries in a database wherein auxiliary data is also used in determining a correct vehicle license plate number, wherein the image is also stored with the auxiliary data, [0035], the confidence level is updated for each auxiliary data that matches a stored record, [0051], [0053], computer processor implemented).
Re Claim 15: As to claim 15, the discussions are addressed with regard to claim 6 respectively. Paul as modified by Kamata and Istenes further discloses wherein the vehicle recognition module is further configured to generate a license plate probability value indicative of a confidence that the license plate number has been correctly identified (see Istenes, [0037]-[0039], by extracting and correctly identifying the entire license plate number in the image, a confidence level is a percentage that defines the likelihood of the entire license plate being correct). See claim 6 for obviousness and motivation statements.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BERNARD KRASNIC whose telephone number is (571)270-1357. The examiner can normally be reached on Mon. - Thur. and every other Friday from 8am - 4pm.
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, Vincent Rudolph can be reached on (571)272-8243. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Bernard Krasnic/Primary Examiner, Art Unit 2671 June 16, 2026
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