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
Claims 1-8 are objected to because of the following informalities: elements throughout each claim are capitalized. Appropriate correction is required.
Claim 7 is objected to because of the following informalities: element (2) has a period not at the end of the claim. Appropriate correction is required.
Claim 7 is objected to because of the following informalities: element (5) has a question mark. Appropriate correction is required.
Claim 8 is objected to because of the following informalities: element (2) has a period not at the end of the claim. Appropriate correction is required.
Claim 8 is objected to because of the following informalities: element (5) has a question mark. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-8 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the enablement requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention.
Regarding claim 1 Applicant discloses, “an Objection Recognition Model (ORM) created and trained by artificial intelligence is provided for the method to use, including information of classification and labelling” on lines 6-8. It is not clear from the specification how the “ORM” is created and trained by AI?
Claims 2-3 and 7 depend from claim 1.
Regarding claim 4 Applicant discloses, “an Objection Recognition Model (ORM) created and trained by artificial intelligence is provided for the method to use, including information of classification and labelling” on lines 8-10. It is not clear from the specification how the “ORM” is created and trained by AI?
Claims 5-6 and 8 depend from claim 4.
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 1-8 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.
The claims are generally narrative and indefinite, failing to conform with current U.S. practice. They appear to be a literal translation into English from a foreign document and are replete with grammatical and idiomatic errors.
Regarding claim 1 Applicant discloses “STORE starting” on line 2. What does this mean?
Regarding claim 1 Applicant discloses “selecting STORE” on line 13. What does this mean?
Regarding claim 1 Applicant discloses “Ending” on line 14. What does this mean?
Claims 2-3 and 7 depend from claim 1.
Regarding claim 2 Applicant discloses “a storage unit database” on line 4. Is this the same component being claimed in claim 1?
Regarding claim 2 Applicant discloses “FETCH starting” on line 2. What does this mean?
Regarding claim 2 Applicant discloses “selecting FETCH” on line 6. What does this mean?
Regarding claim 2 Applicant discloses “Ending” on line 7. What does this mean?
Regarding claim 3 Applicant discloses “a storage unit database” on line 4. Is this the same component being claimed in claim 1?
Regarding claim 3 Applicant discloses “FETCH starting” on line 2. What does this mean?
Regarding claim 3 Applicant discloses “selecting FETCH” on line 6. What does this mean?
Regarding claim 3 Applicant discloses “Ending” on line 7. What does this mean?
Regarding claim 4 Applicant discloses “STORE starting” on line 2. What does this mean?
Regarding claim 4 Applicant discloses “selecting STORE” on line 13. What does this mean?
Regarding claim 4 Applicant discloses “Ending” on line 14. What does this mean?
Claims 5-6 and 8 depend from claim 4.
Regarding claim 5 Applicant discloses “a storage unit database” on line 4. Is this the same component being claimed in claim 4?
Regarding claim 5 Applicant discloses “FETCH starting” on line 2. What does this mean?
Regarding claim 5 Applicant discloses “selecting FETCH” on line 6. What does this mean?
Regarding claim 5 Applicant discloses “Ending” on line 7. What does this mean?
Regarding claim 6 Applicant discloses “a storage unit database” on line 4. Is this the same component being claimed in claim 4?
Regarding claim 6 Applicant discloses “FETCH starting” on line 2. What does this mean?
Regarding claim 6 Applicant discloses “selecting FETCH” on line 6. What does this mean?
Regarding claim 6 Applicant discloses “Ending” on line 7. What does this mean?
Claim 1 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being incomplete for omitting essential steps, such omission amounting to a gap between the steps. See MPEP § 2172.01. The omitted steps are: the structure which would enable the steps. No hardware has been disclosed in the claim language.
Claim 4 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being incomplete for omitting essential steps, such omission amounting to a gap between the steps. See MPEP § 2172.01. The omitted steps are: the structure which would enable the steps. No hardware has been disclosed in the claim language.
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 person shall be entitled to a patent unless –
(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.
Claim(s) 1-8 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by ZHANG (USPGPUB 2023/0331485).
Regarding claim 1, ZHANG discloses an object recognition warehousing method, comprising:
(1) STORE starting (see Figure 1a);
(2) Turning on an image sensor, to capture one or more images of a candidate object to be recognized by the system (see 11-12 in Figure 1a);
(3) Object recognizing in real time, and providing options of classification and labelling; an Objection Recognition Model (ORM) created and trained by artificial intelligence is provided for the method to use, including information of classification and labelling (see paragraph [0031]);
(4) Selecting classification and labelling (see Figure 3);
(5) Optional inputting additional information (see paragraph [0081]);
(6) Selecting a storage unit (see Figure 1a); a storage unit database is provided for the method to use (see Figure 4);
(7) selecting STORE (see Figure 1a); and
(8) Ending (see Figure 1a).
Regarding claim 2, ZHANG discloses the method as claimed in claim 1 further includes a FETCH method comprises:
(1) FETCH starting (see Figure 1a);
(2) Optional inputting a keyword for a candidate object to be fetched (see paragraph [0081]);
(3) Selecting an object (see Figure 1a); a storage unit database with the information for stored objects is provided for the method to use (see Figure 4);
(4) Selecting FETCH (see Figure 1a); and
(5) Ending (see Figure 1a).
Regarding claim 3, ZHANG discloses the method as claimed in claim 1 further includes a FETCH method comprises:
(1) FETCH starting (see Figure 1a);
(2) Selecting a storage unit (see Figure 1a); a storage unit database with the information for stored objects is provided for the method to use (see Figure 4);
(3) Selecting an object (see Figure 1a);
(4) Selecting FETCH (see Figure 1a); and
(5) Ending (see Figure 1a).
Regarding claim 7, ZHANG discloses the method as claimed in claims 1, wherein
the dataset creating process further comprises:
(1) Data collecting, to collect characteristics information for a specific object (see Figures 1a and 3); if
(2) Data augmenting, to increase the amount of data by adding slightly modified copies of already existing data or newly created synthetic data from existing data. It acts as a regularizer and helps reduce overfitting when training a machine learning model to achieve higher accuracy; and
(3) Data labelling: Annotate the features for an object to be identified (see paragraph [0031]); and
(4) Dataset creating (see Figures 1a and 3); and wherein,
the ORM creating and training process further comprises:
(1) Dataset inputting (see Figures 1a and 3);
(2) Image scope defining for an object (see Figures 1 and 3);
(3) Parameters setting (see Figures 1a and 3);
(4) Object Recognition Model (ORM) training (see paragraph [0031]); and
(5) Determining whether it is accurate (see Figures 1a and 3)? and
(6) If yes, an ORM is created; if no, go back to previous step (see Figures 1a and 3).
Regarding claim 4, ZHANG discloses an object recognition warehousing method, comprising:
(1) STORE starting (see Figure 1a);
(2) Selecting a storage unit (see Figure 1a); a storage unit database is provided for the method to use (see Figure 4);
(3) Turning on an image sensor, to capture one or more images of a candidate object to be recognized by the system (see 11-12 in Figure 1a):
(4) Object recognizing in real time, and providing options of classification and labelling: an Objection Recognition Model (ORM) created and trained by artificial intelligence is provided for the method to use (see paragraph [0031]), including information of classification and labelling (see Figure 3);
(5) Selecting classification and labelling (see Figure 3);
(6) Optional inputting additional information (see paragraph [0081]);
(7) selecting STORE (see Figure 1a); and
(8) Ending (see Figure 1a).
Regarding claim 5, ZHANG discloses the method as claimed in claim 4 further includes a FETCH method comprises:
(1) FETCH starting (see Figure 1a);
(2) Inputting a keyword for a candidate object to be fetched (see paragraph [0081]);
(3) Selecting an object (see Figure 1a); a storage unit database with the information for stored objects is provided for the method to use (see Figure 4);
(4) Selecting FETCH (see Figure 1a); and
(5) Ending (see Figure 1a).
Regarding claim 6, ZHANG discloses the method as claimed in claim 4 further includes a FETCH method comprises:
(1) FETCH starting (see Figure 1a);
(2) Selecting a storage unit (see Figure 1a); a storage unit database with the information for stored objects is provided for the method to use (see Figure 4);
(3) Selecting an object (see Figure 1a);
(4) Selecting FETCH (see Figure 1a); and
(5) Ending (see Figure 1a).
Regarding claim 8, ZHANG discloses the method as claimed in claims 4, wherein
the dataset creating process further comprises:
(1) Data collecting, to collect characteristics information for a specific object (see Figures 1a and 3);
(2) Data augmenting, to increase the amount of data by adding slightly modified copies of already existing data or newly created synthetic data from existing data. It acts as a regularizer and helps reduce overfitting when training a machine learning model to achieve higher accuracy (see paragraph [0031]); and
(3) Data labelling; Annotate the features for an object to be identified (see paragraph [0031]); and
(4) Dataset creating (see Figures 1a and 3); and wherein,
the ORM creating and training process further comprises:
(1) Dataset inputting (see Figures 1a and 3);
(2) Image scope defining for an object (see Figures 1a and 3);
(3) Parameters setting (see Figures 1a and 3);
(4) Object Recognition Model (ORM) training (see paragraph [0031]); and
(3) Determining whether it is accurate? (see Figures 1a and 3) and
(6) lf yes, an ORM is created; if no, go back to previous step (see Figures 1a and 3).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL COLLINS whose telephone number is (571)272-8970. The examiner can normally be reached Monday-Friday.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jacob Scott can be reached at (571) 270-3415. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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M.K.C.
10/15/2025
/MICHAEL COLLINS/Primary Examiner, Art Unit 3655