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 § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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) 1,3-6, 9,11-15,17-21,23 are rejected under 35 U.S.C. 103 as being unpatentable over Coppedge (20190108430) in view of Hsiao (20170239576) in further view of Niu et al (20090119090)
As per claim 1, Coppedge (20190108430) teaches a method comprising:
converting, by one or more processors, a first quick response (QR) code to text (as, modifying information associated with a QR code – para 0011; wherein there is text associated with the QR code – para 0042; and a user can be authorized to change the content, including text – para 0053);
and generating, by the one or more processors, a second QR code corresponding to the selected (as, after editing/removing content, generating a new QR code – para 0053, see ‘manager rights’)
As noted above, in relation to the claim elements, Coppedge (20190108430) teaches the concept of taking information from a QR code, deleting/editing/changing information in the QR code, including text, and generating a new QR code based on the changed information. Coppedge (20190108430) discusses editing/changing/reduction of information, including text; but does not define the reduction to be based on phrase replacement; However, Hsiao (20170239576) teaches the analysis of text sentences and phrases, and generating tokens to represent the original phrase/sentence (para 0016), finding equivalent replacements (para 0035) and a selection of words/phrases that has the best certainty metric (para 0085 – and if the percentage is too low, a generated error message shows that the system does track the quality of the matches); and in an example, reducing the amount of words – para 0083, see the replacement phrase for “when the rabbit meets a fox, it is killed”). Therefore, it would have been obvious to one of ordinary skill in the art of phrase replacement to further define the text editing in Coppedge (20190108430) with phrase replacement/reduction, as taught by Hsiao (20170239576) above, because it would advantageously reduce the complexity of the text with a mild sacrifice in accuracy, to represent the natural language text (see Hsiao (20170239576), para 0057). Further to claim 1, the combination of Coppedge (20190108430) in view of Hsiao (20170239576) further teaches ranking the phrases in the set of phrases (see Hsiao (20170239576), as continually updating and determining matching metrics to choose the best combination of words – para 0037). The combination of Coppedge (20190108430) in view of Hsiao (20170239576) does not explicitly teach selecting the phrases based on ranking; however, Niu et al (20090119090) teaches using paraphrasing replacement generation using machine learning frameworks (para 0013, as well as historical – para 0003), using a scoring function that ranks the paraphrasing result (para 0032), which can be in the form of phrases, among other structures (para 0018). Therefore, it would have been obvious to one of ordinary skill in the art of phrase replacement to modify the algorithms of Hsiao (20170239576) (found in the combination of Coppedge (20190108430) in view of Hsiao (20170239576)) with a machine learning model replacing existing text with ranked paraphrase substitutes, as taught by Niu et al (20090119090), because it would advantageously allow for processing of a broader/varying classes of paraphrasing while generating a recursive automated system (Niu et al (20090119090) , para 0013)
As per claim 3, the combination of Coppedge (20190108430) in view of Hsiao (20170239576) in further view of Niu et al (20090119090) teaches the method of claim 2, wherein the ranking of the phrases comprises providing the phrases as inputs to a trained machine-learning model (see Hsiao (20170239576), using machine learning for the comparison/learning of grammar phrases – para 0057).
As per claim 4, the combination of Coppedge (20190108430) in view of Hsiao (20170239576) in further view of Niu et al (20090119090) teaches the method of claim 2, further comprising: identifying verbs in the phrases in the set of phrases; wherein the ranking of the phrases is based on the identified verbs (see Hsiao (20170239576), para 0059, identifying verbs, and para 0056, verb tenses; each used in ranking/choosing the best match – para 0037 – using ‘high certain metrics’).
As per claim 5, the combination of Coppedge (20190108430) in view of Hsiao (20170239576) in further view of Niu et al (20090119090) teaches the method of claim 1, further comprising: identifying sentences in the set of phrases (see Hsiao (20170239576), as using the grammar to generate all possible sentences – para 0036).
As per claim 6, the combination of Coppedge (20190108430) in view of Hsiao (20170239576) in further view of Niu et al (20090119090) teaches the method of claim 1, further comprising: training a machine-learning model using natural language text and shortened versions of the natural language text, wherein the ranking of the phrases in the set of phrases is performed by the trained machine-learning mode (see Hsiao (20170239576), using machine learning for the comparison/learning of grammar phrases – para 0057, and natural language format – para 0035, the natural language compiler; and Niu et al (20090119090) teaching the ranking/paraphrasing – para 0032 using machine learning based paraphrasing – para 0013).
As per claim 7, the combination of Coppedge (20190108430) in view of Hsiao (20170239576) in further view of Niu et al (20090119090) teaches the method of claim 1, wherein the first QR code is a version 40 QR code (see Coppedge (20190108430), teaching QR code and other 2 dimensional machine readable bar codes – para 0004; examiner notes that “QR code” is understood to include various versions of QR code protocols, and note that is old and well known in the art that version 40 QR code is an example of QR codes; examiner notes, as proof of this, see Tankleff (20140175163) teaches the use of version 40 QR code as part of QR code processing – para 0003).
Claims 9,11-14 are system claims whose steps are performed by method steps 1,3-6 above and as such, claims 9,11-14 are similar in scope and content to claims 1,3-6 above; therefore, claims 9,11-14 are rejected under similar rationale as presented against claims 1,3-6 above. Furthermore, Coppedge (20190108430) teaches processor (para 0013) and memory (para 0018), performing the steps.
Claims 15,17-20 are non-transitory computer readable medium claims, performing steps similar in scope and content to method claims 1,3-6 above; therefore, claims 15-20 are rejected under similar rationale as presented against claims 1,3-6 above. Coppedge (20190108430) further teaches computer readable medium, storing the steps – para 0018.
As per claims 21,23, examiner notes that the claimed “version 40 QR code” is a subset/definition of QR codes (ie, QR codes can range from 1x1 up to a dimensionality of 40); Coppedge (20190108430), with recited sections to QR codes in claim 1, does not limit the dimensionality of the QR code and therefore, the QR codes of Coppedge (20190108430) includes version 40 QR code -- examiner notes, as proof of this, see Tankleff (20140175163) teaches the use of version 40 QR code as part of QR code processing – para 0003).
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Coppedge (20190108430) in view of Hsiao (20170239576) in view of Niu et al (20090119090) in further view of Miyazawa et al (5983186)
As per claim 22, dependent upon claim 9, the mapping to the elements of claim 9 is as follows:
Coppedge (20190108430) teaches a system comprising a memory that stores instructions (para 0018,0019), and one or more processors (para 0013) configured by the instructions to perform operations comprising:
Converting a first quick response (QR) code to text (as, modifying information associated with a QR code – para 0011; wherein there is text associated with the QR code – para 0042; and a user can be authorized to change the content, including text – para 0053);
and generating a second QR code corresponding to the selected (as, after editing/removing content, generating a new QR code – para 0053, see ‘manager rights’)
As noted above, in relation to the claim elements, Coppedge (20190108430) teaches the concept of taking information from a QR code, deleting/editing/changing information in the QR code, including text, and generating a new QR code based on the changed information. Coppedge (20190108430) discusses editing/changing/reduction of information, including text; but does not define the reduction to be based on phrase replacement; However, Hsiao (20170239576) teaches the analysis of text sentences and phrases, and generating tokens to represent the original phrase/sentence (para 0016), finding equivalent replacements (para 0035) and a selection of words/phrases that has the best certainty metric (para 0085 – and if the percentage is too low, a generated error message shows that the system does track the quality of the matches); and in an example, reducing the amount of words – para 0083, see the replacement phrase for “when the rabbit meets a fox, it is killed”). Therefore, it would have been obvious to one of ordinary skill in the art of phrase replacement to further define the text editing in Coppedge (20190108430) with phrase replacement/reduction, as taught by Hsiao (20170239576) above, because it would advantageously reduce the complexity of the text with a mild sacrifice in accuracy, to represent the natural language text (see Hsiao (20170239576), para 0057). Further to claim 1, the combination of Coppedge (20190108430) in view of Hsiao (20170239576) further teaches ranking the phrases in the set of phrases (see Hsiao (20170239576), as continually updating and determining matching metrics to choose the best combination of words – para 0037). The combination of Coppedge (20190108430) in view of Hsiao (20170239576) does not explicitly teach selecting the phrases based on ranking; however, Niu et al (20090119090) teaches using paraphrasing replacement generation using machine learning frameworks (para 0013, as well as historical – para 0003), using a scoring function that ranks the paraphrasing result (para 0032), which can be in the form of phrases, among other structures (para 0018). Therefore, it would have been obvious to one of ordinary skill in the art of phrase replacement to modify the algorithms of Hsiao (20170239576) (found in the combination of Coppedge (20190108430) in view of Hsiao (20170239576)) with a machine learning model replacing existing text with ranked paraphrase substitutes, as taught by Niu et al (20090119090), because it would advantageously allow for processing of a broader/varying classes of paraphrasing while generating a recursive automated system (Niu et al (20090119090) , para 0013).
As to the claim elements toward selecting the phrases based on the amount of compression/available memory, Miyazawa et al (593186) teaches the concept of a recognized phrase list to be limited by the size of available memory (see Miyazawa et al (593186), col. 7 lines 1-6). Therefore, it would have been obvious to one of ordinary skill in the art of phrase replacement and compression to modify the combination of Coppedge (20190108430) in view of Hsiao (20170239576) in further view of Niu et al (20090119090) with text/phrase restrictions due to memory constraints, as taught by Miyazawa et al (593186), because it would advantageously be adaptable for the system constraints, such as memory size, system power, etc. (see Miyazawa et al (593186), col. 2 lines 10-29).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Coppedge (20190108430) in view of Hsiao (20170239576) in further view of Gluck et al (20180107678).
As per claim 8, the combination of Coppedge (20190108430) in view of Hsiao (20170239576) teaches content reduction (see Coppedge (20190108430), para 0053, manager rights, reducing/deleting content), but the combination of Coppedge (20190108430) in view of Hsiao (20170239576) does not teach removal of phrases/words to establish compression/reduction of information; Gluck et al (20180107678) teaches the reduction of words/phrases from the text/document (para 0013). Therefore, it would have been obvious to one of ordinary skill in the art of efficient text storage/processing to enhance the text reduction of the combination of Coppedge (20190108430) in view of Hsiao (20170239576) with focused words/phrase reduction, as taught by Gluck et al (20180107678) above, because it would advantageously reduce the storage size of the document, and compression could be achieved (Gluck et al (20180107678), para 0013, 0059).
Response to Arguments
Applicant’s arguments with respect to the claim(s) have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Examiner notes the use of the Niu et al (20090119090) reference to teach the use of machine learning algorithms to generate replacement phrases and selection based on ranking of the phrases; and the introduction of the Miyazawa et al (593186) reference to teach the concept of a phrase list limited by the amount of available memory (ie, compressing the list to fit into the available memory space).
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.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
The following references were found toward, applicants claim scope and spec:
Shevchenko et al (11880644) teaches phrase matching and classification, based on machine learning algorithms – para 0054, 0057
Bao (20130197992) teaches editable QR codes, generating anew QR code with edited/deleted information (para 0150,0158,0162)
Roux et al (20210303809) teaches QR code generation after a compression of edited information (para 0018)
Samaras et al (20170102848) teaches editing of content and properties of QR codes (para 004, 0122)
Mane (20200320167) teaches replacement of keyword/phrases with smaller sized symbols (para 0046)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michael Opsasnick, telephone number (571)272-7623, who is available Monday-Friday, 9am-5pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Mr. Richemond Dorvil, can be reached at (571)272-7602. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Michael N Opsasnick/Primary Examiner, Art Unit 2658 09/17/2024