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 .
Response to Preliminary Amendment
In response to applicant’s preliminary amendment received on 2/14/24, all requested changes to the claims have been entered. Claim 1 was previously pending. Claims 2-20 have been added. Claims 1-20 are currently pending.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. Applicant has not complied with one or more conditions for receiving the benefit of an earlier filing date under 35 U.S.C. 121 as follows:
The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
The disclosure of the prior-filed applications, Application No. 17/506006; 16/530778; 15/483291; 14/713863 and 62/085237, fail to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application. None of the prior-filed applications discloses “training a neural network” or “identifying…via the neural network” as disclosed in independent claim 1, and similarly independent claim 9. The prior-filed applications disclose “machine learning” techniques and algorithms (e.g. k-nearest neighbors or “kNN”) however a neural network is a different, specific type of, machine learning technique which is not disclosed in the prior filed applications. As such none of claims 1-16 are afforded the benefit of the prior applications filing dates, and therefore the effective filing date of those particular claims is 12/5/23. Therefore any patent or publication including the inventors own work published more than a year prior to 12/5/23 can and is used as a basis for a prior art rejection herein.
However, the Examiner notes that with regards to independent claim 17, while the prior-filed applications do not use the phrase “artificial intelligence model” they do disclose “machine learning” techniques and algorithms which are known in the art as a subset of artificial intelligence models. As such, claims 17-20 are afforded the benefit the benefit of the prior application filing dates, giving them an effective filing date of 11/26/14.
Examiner’s Comment on 35 USC § 101
Independent claims 1, 9 and 17 disclose concepts that can be identified as abstract ideas by the courts. For example, the mental process of identifying closest matches between a filled-in form and a set of forms, and registering/aligning the filled-in instance with the closest matches. However, the claims are not directed to the judicial exception because the exception is integrated into a practical application because:
(1) there is teaching in the specification about how the claimed invention improves a computer, other technology or technical field (“without reference to what is well-understood, routine, conventional activity” (MPEP 2106.04(d)(1)), for example, see the specification at least in paragraphs 38-39, 44, 47 and 51, where the difficulties associated with the technical field of digitally logging and reporting data in the health care field is improved through the automated recognition of an unknown filled-in form by retrieving top matching forms, significantly less than all possible forms, through the use of machine learning techniques; and
(2) the claimed invention, as a whole, defines particular solution to a problem, such that the claim discloses identifying a set of closest matches between a filled-in instance of a form and the set of forms via a neural network trained with one copy of each of a set of forms.
Claim Rejections - 35 USC § 112(a)
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 7 and 15 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Specifically, the limitation “scoring the bag of visual words query vector against each of the set of support vectors via the neural network” added as part of both new claims 7 and 15 on 2/14/24 is not described in the specification filed on 12/5/23 in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim Rejections - 35 USC § 112(b)
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 8 and 16 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.
Claim 8 recites that “identifying is performed via an Oriented FAST Rotated BRIEF (ORB) feature detector”. However, claim 1, from which claim 8 depends, has already established that identifying is performed via the neural network. An ORB is not a type of neural network and therefore the contradiction makes the claim vague and indefinite.
Claim 16 recites that “identifying is performed via an Oriented FAST Rotated BRIEF (ORB) feature detector”. However, claim 9, from which claim 16 depends, has already established that identifying is performed via the neural network. An ORB is not a type of neural network and therefore the contradiction makes the claim vague and indefinite.
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 17-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US 2005/0238257 to Kaneda et al. (“Kaneda”).
Regarding claim 17, Kaneda discloses a method comprising:
training an artificial intelligence model with one copy of each of a set of forms (Fig. 4, 9, and 11; paragraphs 42, 53 and 78, wherein the combined processes of template form registration (Fig. 4) and searching (Fig. 11) correspond to the broadest reasonable interpretation of an “artificial intelligence model”, wherein during form registration one copy of form template is processed and registered corresponding to “training” the model for use in input form searching);
extracting a plurality of key points from a query instance of a filled in form (Figs. 5, Figs 9 and 11, element s402; paragraphs 54 and 80, area segmentation of an input form (i.e. query instance of a filled in form) correspond to extracting a plurality of key points);
identifying a set of closest matches between a filled-in instance of a form and the set of forms via a query to the artificial intelligence model including the extracted key points (Fig. 11, element s1103; Fig. 12; paragraphs 79, 81 and 83-87, wherein a set of closest/approximate matching forms is identified based on a comparison of extracted key points (segmented areas) from the input form (i.e. filled-in instance of a form) with those of the form templates registered, this process of identifying being performed as a part of the form searching portion (Fig. 11) of the “artificial intelligence model”); and
registering the filled-in instance of the form with the set of closest matches (Figs. 9 and 11, elements s404 and s405; paragraphs 55, 56, 82, wherein the set of closest matches are subjected to further registration/matching).
Regarding claim 18, Kaneda discloses the method of claim 17, wherein the set of closest matches comprises a predetermined maximum number of closest matches (Fig. 12, element S1214; paragraph 86, wherein the max number corresponds to total number of form templates).
Regarding claim 19, Kaneda discloses the method of claim 17, wherein the set of closest matches comprises each of the set of forms that exceed a threshold matching score with the filled-in instance of the form (Fig. 12, elements S1204-S1210; paragraphs 83-85, wherein the if the comparison is equal to or smaller than the comparison reference input in S1101 (i.e. threshold matching score) then the template and input are considered a close/approximate match).
Regarding claim 20, Kaneda discloses the method of claim 17, wherein the set of forms are form templates (Fig. 4; paragraph 42, the set of forms registered are template forms).
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 1-16 are rejected under 35 U.S.C. 103 as being unpatentable over US 2016/0148074 to Jean et al. (“Jean”) in view of USPN 10,769,503 to Buhler et al. (“Buhler”).
Regarding claim 1, Jean discloses a method comprising:
training a Fig. 18 and paragraph 88-90, 134, 143 and 175, wherein a machine learning algorithm, such as k-mean or k-nearest neighbor implemented visual vocabulary, is trained with copies of each form);
identifying a set of closest matches between a filled-in instance of a form and the set of forms via the Fig. 18, element 1830, paragraphs 134 and 175, wherein the query image (i.e. filled-in form) is processed by the trained visual vocabulary to identify a set of closest matching form templates); and
registering the filled-in instance of the form with the set of closest matches (Fig. 18, element 1835; paragraphs 134 and 175, wherein matching (1835) registers all candidate form templates against the query (i.e. filled-in instance of the form)).
Jean does not disclose expressly use of a “neural network” in regards to the “training of a neural network” and “identifying a set of closest matches…via the neural network”.
Buhler discloses training a neural network for identifying a set of closest matching documents (i.e. forms) using a neural network using a “bag of words” (column 15, line 20 – column 16, line 30 and column 28, lines 30-52).
Jean & Buhler are combinable because they are from the same art of document/form image processing.
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to incorporate the technique of identifying a set of closest matching documents/forms using a trained neural network, as taught by Buhler, into the method identifying a set of closest matching forms using a trained machine learning algorithm disclosed by Jean.
The suggestion/motivation for doing so would have been to provide automatic analysis and organization of printed documents (Buhler, column 1, lines 21-22).
Therefore, it would have been obvious to combine Buhler with Jean to obtain the invention as specified in claim 1.
Regarding claim 2, the combination of Jean and Buhler discloses the method of claim 1, further comprising: orienting the filled-in form to the set of closest matches via a Speed Up Robust Features (SURF) (Jean, paragraphs 63, 114, 115).
Regarding claim 3, the combination of Jean and Buhler discloses the method of claim 1, further comprising: orienting the filled-in form to the set of closest matches via a Scale Invariant Feature Transform (SIFT) with 100 key points (Jean, paragraphs 63, 114, 115).
Regarding claim 4, the combination of Jean and Buhler discloses the method of claim 1, wherein the set of closest matches comprises a predetermined maximum number of closest matches (Jean, paragraphs 115 and 131-132, wherein the top “h” or “H” candidate matches are determined, “h” or “H” corresponding to a maximum number).
Regarding claim 5, the combination of Jean and Buhler discloses the method of claim 1, wherein the set of closest matches comprises each of the set of forms that exceed a threshold matching score with the filled-in instance of the form (Jean, paragraph 165, wherein global alignment threshold corresponds to the threshold as claimed).
Regarding claim 6, the combination of Jean and Buhler discloses the method of claim 1, wherein the set of forms are form templates (Jean, paragraph 134, wherein templates correspond to claimed form templates).
Regarding claim 7, the combination of Jean and Buhler discloses the method of claim 1, wherein said identifying the set of closest matches further comprises:
generating a set of bag of visual words support vectors describing graphical dimensions of each of the one copy of each of the set of forms (Jean, Figs. 8, 9; paragraphs 88-93, 134, 141, 153, 155);
generating a bag of visual words query vector describing graphical dimensions of the filled-in instance of the form (Jean, Fig. 10; paragraphs 91 and 94-95, 134, 141, 153, 155); and
scoring the bag of visual words query vector against each of the set of support vectors via the neural network (Jean, paragraphs 100 and 134; Buhler, column 15, line 20 – column 16, line 30 and column 28, lines 30-52, as noted above with regards to claim 1, Buhler discloses scoring a bag of words query against template via a neural network).
Regarding claim 8, the combination of Jean and Buhler discloses the method of claim 1, wherein the identifying is performed via an Oriented FAST Rotated BRIEF (ORB) feature detector (Jean, paragraphs 63, 64).
Regarding claim 9, Jean discloses the system comprising:
a processor (Fig. 31, element 3110 and paragraph 178); and
a memory (Fig. 31, element 3111) including Fig. 18 and paragraph 88-90, 134, 143 and 175, wherein a machine learning algorithm, such as k-mean or k-nearest neighbor implemented visual vocabulary, is trained with copies of each form) and including instructions that when executed cause the processor (paragraph 178) to;
identify a set of closest matches between a filled-in instance of a form and the set of forms via the Fig. 18, element 1830, paragraphs 134 and 175, wherein the query image (i.e. filled-in form) is processed by the trained visual vocabulary to identify a set of closest matching form templates); and
register the filled-in instance of the form with the set of closest matches (Fig. 18, element 1835; paragraphs 134 and 175, wherein matching (1835) registers all candidate form templates against the query (i.e. filled-in instance of the form)).
Jean does not disclose expressly use of a “neural network” in regards to the “training of a neural network” and “identifying a set of closest matches…via the neural network”.
Buhler discloses training a neural network for identifying a set of closest matching documents (i.e. forms) using a neural network using a “bag of words” (column 15, line 20 – column 16, line 30 and column 28, lines 30-52).
Jean & Buhler are combinable because they are from the same art of document/form image processing.
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to incorporate the technique of identifying a set of closest matching documents/forms using a trained neural network, as taught by Buhler, into the method identifying a set of closest matching forms using a trained machine learning algorithm disclosed by Jean.
The suggestion/motivation for doing so would have been to provide automatic analysis and organization of printed documents (Buhler, column 1, lines 21-22).
Therefore, it would have been obvious to combine Buhler with Jean to obtain the invention as specified in claim 9.
The limitations of claims 10-16 are substantially similar to those of claims 2-8, respectively. Therefore, the rejections applied to claims 2-8 also apply equally to claims 10-16.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
See attached PTO-892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AARON W CARTER whose telephone number is (571)272-7445. The examiner can normally be reached 8am - 5pm (Mon - Fri).
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/AARON W CARTER/Primary Examiner, Art Unit 2661