Prosecution Insights
Last updated: April 19, 2026
Application No. 18/840,966

SIGNAL COMPRESSION APPARATUS, SIGNAL RECONSTRUCTION APPARATUS, AND SIGNAL PROCESSING SYSTEM

Non-Final OA §102
Filed
Aug 23, 2024
Examiner
MAI, LAM T
Art Unit
2845
Tech Center
2800 — Semiconductors & Electrical Systems
Assignee
Osaka University
OA Round
1 (Non-Final)
96%
Grant Probability
Favorable
1-2
OA Rounds
1y 9m
To Grant
97%
With Interview

Examiner Intelligence

Grants 96% — above average
96%
Career Allow Rate
963 granted / 1003 resolved
+28.0% vs TC avg
Minimal +1% lift
Without
With
+0.6%
Interview Lift
resolved cases with interview
Fast prosecutor
1y 9m
Avg Prosecution
20 currently pending
Career history
1023
Total Applications
across all art units

Statute-Specific Performance

§101
14.2%
-25.8% vs TC avg
§103
17.4%
-22.6% vs TC avg
§102
30.6%
-9.4% vs TC avg
§112
13.9%
-26.1% vs TC avg
Black line = Tech Center average estimate • Based on career data from 1003 resolved cases

Office Action

§102
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 § 102 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)(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 and 6-7 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Fira Monica et al (ISSN: 2079-6374, DOI: 10.3390/bios 12030146; Biosensors, Vol 12, No. 3, Page 146, XP093270944). Regarding claim 1, Fira Monica discloses in architecture in figure 3 that teaches: A signal reconstruction apparatus (the apparatus implementing the ECG signal reconstruction in Figure 3) comprising: a reception part that receives a compression signal obtained by compressing a target signal by use of an observation matrix; (see projection matrix Ф in figure 3, delivering compressed signal y~ from the target ECG signal x) and a reconstruction part that reconstructs the received compression signal, wherein the reconstruction part includes the observation matrix, (Basis Pursuit in figure 3, see also section 2, page 4, item (ii) and equation (4), and pseudocode bridging pages 4 and 5) and a dictionary matrix (mega-dictionary φ or specific dictionary φᵢ used for reconstruction in fig. 3) in which a past signal is arranged in each column, (the atoms are the columns of each dictionary matrix Ʊ) the past signal being a signal of a same type as the target signal and being a target signal obtained in advance for a plurality of times, (see 3.3.1 "In order to build patient-specific dictionaries, we used the first minutes of each patient's record and then the rest of the ECG signal was used for testing. Thus, the atoms represent ECG segments of size 300,", 3.3.2 "The mega-dictionary used consists of 1472 atoms (i.e., 184 beats from each of the 8 classes discussed, 7 pathological and the normal beat class).", or 3.3.3 "Thus, analyzing 7 pathological classes and the normal class, we built 8 dictionaries, each with 700 atoms specific to each class") obtains an estimation vector by inputting the received compression signal, the observation matrix, and the dictionary matrix to a reconstruction algorithm execution module, and derives a reconstruction signal corresponding to the target signal by inputting the obtained estimation vector and dictionary matrix to a calculation module for obtaining a product. (see reconstructed ECG signal and reconstruction stage, step 2 and step 3 of pseudocode bridging pages 4 and 5). Regarding claim 6, claim 6 is similar to claim 1 therefore, claim 6 should be rejected as well as rejected in claim 1, such as: Fira Monica discloses in architecture in figure 3 that teaches: A signal reconstruction apparatus (the apparatus implementing the ECG signal reconstruction in Figure 3) comprising: a reception part that receives a compression signal obtained by compressing a target signal by use of an observation matrix; (see projection matrix Ф in figure 3, delivering compressed signal y~ from the target ECG signal x) and a reconstruction part that reconstructs the received compression signal, wherein the reconstruction part includes the observation matrix, (Basis Pursuit in figure 3, see also section 2, page 4, item (ii) and equation (4), and pseudocode bridging pages 4 and 5) and a dictionary matrix (mega-dictionary φ or specific dictionary φᵢ used for reconstruction in fig. 3) in which a past signal is arranged in each column, (the atoms are the columns of each dictionary matrix Ʊ) the past signal being a signal of a same type as the target signal and being a target signal obtained in advance for a plurality of times, (see 3.3.1 "In order to build patient-specific dictionaries, we used the first minutes of each patient's record and then the rest of the ECG signal was used for testing. Thus, the atoms represent ECG segments of size 300,", 3.3.2 "The mega-dictionary used consists of 1472 atoms (i.e., 184 beats from each of the 8 classes discussed, 7 pathological and the normal beat class).", or 3.3.3 "Thus, analyzing 7 pathological classes and the normal class, we built 8 dictionaries, each with 700 atoms specific to each class") obtains an estimation vector by inputting the received compression signal, the observation matrix, and the dictionary matrix to a reconstruction algorithm execution module, and derives a reconstruction signal corresponding to the target signal by inputting the obtained estimation vector and dictionary matrix to a calculation module for obtaining a product. (see reconstructed ECG signal and reconstruction stage, step 2 and step 3 of pseudocode bridging pages 4 and 5). Regarding claim 7, Fira Monica discloses in architecture in figure 3 that teaches: A signal reconstruction apparatus (the apparatus implementing the ECG signal reconstruction in Figure 3) comprising: a reception part that receives a compression signal obtained by compressing a target signal by use of an observation matrix; (see projection matrix Ф in figure 3, delivering compressed signal y~ from the target ECG signal x) and a reconstruction part that reconstructs the received compression signal, wherein the reconstruction part includes the observation matrix, (Basis Pursuit in figure 3, see also section 2, page 4, item (ii) and equation (4), and pseudocode bridging pages 4 and 5) and a dictionary matrix (mega-dictionary φ or specific dictionary φᵢ used for reconstruction in fig. 3) in which a past signal is arranged in each column, (the atoms are the columns of each dictionary matrix Ʊ) the past signal being a signal of a same type as the target signal and being a target signal obtained in advance for a plurality of times, (see 3.3.1 "In order to build patient-specific dictionaries, we used the first minutes of each patient's record and then the rest of the ECG signal was used for testing. Thus, the atoms represent ECG segments of size 300,", 3.3.2 "The mega-dictionary used consists of 1472 atoms (i.e., 184 beats from each of the 8 classes discussed, 7 pathological and the normal beat class).", or 3.3.3 "Thus, analyzing 7 pathological classes and the normal class, we built 8 dictionaries, each with 700 atoms specific to each class") obtains an estimation vector by inputting the received compression signal, the observation matrix, and the dictionary matrix to a reconstruction algorithm execution module, and derives a reconstruction signal corresponding to the target signal by inputting the obtained estimation vector and dictionary matrix to a calculation module for obtaining a product. (see reconstructed ECG signal and reconstruction stage, step 2 and step 3 of pseudocode bridging pages 4 and 5). Allowable Subject Matter Claim 2 is objected to as being dependent upon a rejected base claim, but it would be considered for allowable if it is rewritten in independent form including all of the limitations of the base claim and any intervening claims. Closest prior art of record, considered individually or in combination, fails to fairly teach or suggest objected features, which is: wherein the dictionary matrix is configured so that highly correlated signals are placed in adjacent columns, with respect to past signals for the plurality of times. Claim 3 is objected to as being dependent upon a rejected base claim, but it would be considered for allowable if it is rewritten in independent form including all of the limitations of the base claim and any intervening claims. Closest prior art of record, considered individually or in combination, fails to fairly teach or suggest objected features, which is: wherein the dictionary matrix is configured so that the highly correlated signals, among the past signals for the plurality of times, are selected and placed. Claim 4 is objected to as being dependent upon a rejected base claim, but it would be considered for allowable if it is rewritten in independent form including all of the limitations of the base claim and any intervening claims. Closest prior art of record, considered individually or in combination, fails to fairly teach or suggest objected features, which is: wherein the dictionary matrix is configured so that average frequencies are placed in order of height, with respect to past signals for the plurality of times. Claim 5 is objected to as being dependent upon a rejected base claim, but it would be considered for allowable if it is rewritten in independent form including all of the limitations of the base claim and any intervening claims. Closest prior art of record, considered individually or in combination, fails to fairly teach or suggest objected features, which is: wherein the dictionary matrix is configured so that a signal having an average frequency with a high occurrence rate, among the past signals for the plurality of times, is selected and placed. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to LAM T MAI whose telephone number is (571)272-1807. The examiner can normally be reached Monday-Friday 6am-2pm eastern time. 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, Dameon Levi can be reached at 571 272-2105. 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. /LAM T MAI/Primary Examiner, Art Unit 2845
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Prosecution Timeline

Aug 23, 2024
Application Filed
Mar 03, 2026
Non-Final Rejection — §102 (current)

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Study what changed to get past this examiner. Based on 5 most recent grants.

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Prosecution Projections

1-2
Expected OA Rounds
96%
Grant Probability
97%
With Interview (+0.6%)
1y 9m
Median Time to Grant
Low
PTA Risk
Based on 1003 resolved cases by this examiner. Grant probability derived from career allow rate.

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