DETAILED ACTION
Claims 1-20 are currently pending.
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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No.17/518, 191, filed on 10/27/2022.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/23/2024 has been considered by the Examiner.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-4, 8-11, and 15-18 of U.S. Patent No. 12,118,813. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims correspond as discussed below.
Regarding claim 1,
A method, comprising:
receiving, by a processing device, one or more documents; (‘813 claim 1, receiving, by a processing device, one or more documents)
identifying a plurality of symbols in the one or more documents; (‘813, claim 1, performing optical character recognition on the one or more documents to detect words comprising symbols in the one or more documents)
determining a plurality of encoding values, wherein each encoding value of the plurality of encoding values corresponds to a respective symbol of the plurality of symbols; (‘813 determining an encoding value for each of the symbols)
generating, based on the plurality of encoding values, a vector array comprising a set of hashed symbol values; (‘813 claim 1, applying a second hash function to each hashed symbol value of the first set of hashed symbol values to generate a vector array comprising a second set of hashed symbol values) )
applying a predefined transformation to each value of the set of hashed symbol values of the vector array; (‘813 claim 1, applying a linear transformation to each value of the second set of hashed values of the vector array) and
applying an activation function to the vector array to obtain a plurality of feature values associated with the plurality of symbols. (‘813 claim 1, applying an irreversible non-linear activation function to the vector array to obtain abstract values associated with the symbols)
Regarding claim 2,
The method of claim 1, further comprising: training a neural network to detect document fields using the plurality of feature values. (‘813 claim 2, training a neural network to detect field in the input document using the saved abstract values)
Regarding claim 3,
The method of claim 2, further comprising: utilizing the neural network to detect document fields in an input document. (‘813 claim 2, training a neural network to detect field in the input document using the saved abstract values)
Regarding claim 4,
The method of claim 1, wherein the encoding value is a Unicode value. (‘813 claim 3, wherein the encoding value is a Unicode value)
Regarding claim 5,
The method of claim 1, wherein each encoding value is hashed by hash function that performs a summation of a whole number and a remainder of a division of the encoding value and the whole number. (‘813 claim 4, wherein the first hash function is a summation of a whole number and a remainder of a division of the encoding value and the whole number)
Regarding claim 6,
The method of claim 1, wherein the activation function is a non-linear activation function. (‘813 claim 1, applying an irreversible non-linear activation function to the vector array to obtain abstract values associated with the symbols)
Regarding claim 7,
The method of claim 1, wherein the predefined transformation is a linear transformation. (‘813 claim 1, applying a linear transformation to each value of the second set of hashed values of the vector array)
Regarding claim 8,
A system, comprising:
a memory; (‘813 claim 8, a memory )
a processing device coupled to the memory, the processing device to: (‘813 claim 8, a processor coupled to the memory, the processor configured to)
receive one or more documents; (‘813 claim 8, receive one or more documents)
identify a plurality of symbols in the one or more documents; (‘813 claim 8, perform optical character recognition one the one or more documents to detect words comprising symbols in the one or more documents)
determine a plurality of encoding values, wherein each encoding value of the plurality of encoding values corresponds to a respective symbol of the plurality of symbols; (‘813 claim 8, determine a encoding value for each of the symbols)
generate, based on the plurality of encoding values, a vector array comprising a set of hashed symbol values; (‘813 claim 8, apply a second hash function to each hashed symbol value of the first set of hashed symbol values to generate a vector array comprising a second set of hashed symbol values)
apply a predefined transformation to each value of the set of hashed symbol values of the vector array; (‘813 claim 8, apply a linear transformation to each value of the second set of hashed symbol values of the vector array) and
apply an activation function to the vector array to obtain a plurality of feature values associated with the plurality of symbols. (‘813 claim 8, apply an irreversible non-linear activation function to the vector array to obtain abstract values associated with the symbols)
Regarding claim 9,
The system of claim 8, wherein the processor is further configured to: train a neural network to detect document fields using the plurality of feature values. (‘813 claim 9, train a neural network to detect fields in the input document using the saved abstract values)
Regarding claim 10,
The system of claim 9, wherein the processor is further configured to: utilize the neural network to detect document fields in an input document. (‘813 claim 9, train a neural network to detect fields in the input document using the saved abstract values)
Regarding claim 11,
The system of claim 8, wherein the encoding value is a Unicode value. (‘813 claim 10, wherein the encoding value is a Unicode value)
Regarding claim 12,
The system of claim 8, wherein each encoding value is hashed by hash function that performs a summation of a whole number and a remainder of a division of the encoding value and the whole number. (‘813 claim 11, wherein the first hash function is a summation of a whole number and a remainder of a division of the encoding value and the whole number)
Regarding claim 13,
The system of claim 8, wherein the activation function is a non-linear activation function. (‘813 claim 8, apply an irreversible non-linear activation function to the vector array to obtain abstract values associated with the symbols)
Regarding claim 14,
The system of claim 8, wherein the predefined transformation is a linear transformation. (‘813 claim 8, apply a linear transformation to each value of the second set of hashed symbol values of the vector array)
Regarding claim 15,
A non-transitory computer-readable storage medium comprising executable instructions that, when executed by a processing device, cause the processing device to: (‘813 claim 15, a non-transitory machine-readable storage medium including instructions that, when accessed by a processing device, cause the processing device to)
receive one or more documents; (‘813 claim 15, receive one or more documents)
identify a plurality of symbols in the one or more documents; (‘813 claim 15, perform optical character recognition on the one or more documents to detect words comprising symbols in the one or more documents)
determine a plurality of encoding values, wherein each encoding value of the plurality of encoding values corresponds to a respective symbol of the plurality of symbols; (‘813 claim 15, determine a encoding value for each of the symbols)
generate, based on the plurality of encoding values, a vector array comprising a set of hashed symbol values; (‘813 claim 15, apply a second hash function to each hashed symbol value of the first set of hashed symbol values to generate a vector array comprising a second set of hashed symbol values)
apply a predefined transformation to each value of the set of hashed symbol values of the vector array; (‘813 claim 15, apply a linear transformation to each value of the second set of hashed symbol values in the vector array)
and
apply an activation function to the vector array to obtain a plurality of feature values associated with the plurality of symbols. (‘813 claim 15, apply an irreversible non-linear activation function to the vector array to obtain abstract values associated with the symbols)
Regarding claim 16,
The non-transitory computer-readable storage medium of claim 15, further comprising executable instructions that, when executed by the processing device, cause the processing device to: train a neural network to detect document fields using the plurality of feature values. (‘813 claim 16, train a neural network to detect fields in the input document using the saved abstract values)
Regarding claim 17,
The non-transitory computer-readable storage medium of claim 15, wherein the encoding value is a Unicode value. (‘813 claim 17, wherein the encoding value is a Unicode value)
Regarding claim 18,
The non-transitory computer-readable storage medium of claim 15, wherein each encoding value is hashed by hash function that performs a summation of a whole number and a remainder of a division of the encoding value and the whole number. (‘813 claim 18, wherein the first hash function is a summation of a whole number and a remainder of a division of the encoding value and the whole number )
Regarding claim 19,
The non-transitory computer-readable storage medium of claim 15, wherein the activation function is a non-linear activation function. (‘813 claim 15, apply an irreversible non-linear activation function to the vector array to obtain abstract values associated with the symbols)
Regarding claim 20,
The non-transitory computer-readable storage medium of claim 15, wherein the predefined transformation is a linear transformation.
(‘813 claim 15, apply a linear transformation to each value of the second set of hashed symbol values in the vector array)
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: the processing device in claim 8.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Allowable Subject Matter
The claims are not rejected under the prior art and would be in condition for allowance if the above double patenting rejections were overcome.
Regarding claim 1, neither the closest known prior art, nor any reasonable combination thereof, teaches:
determining a plurality of encoding values, wherein each encoding value of the plurality of encoding values corresponds to a respective symbol of the plurality of symbols;
generating, based on the plurality of encoding values, a vector array comprising a set of hashed symbol values;
applying a predefined transformation to each value of the set of hashed symbol values of the vector array; and
applying an activation function to the vector array to obtain a plurality of feature values associated with the plurality of symbols.
Jha (US 2022/0114644) teaches the use of a hash encoder to determine a sparse representation of an input. See [0045]. Jha uses a plurality of hash encoders to create predictions, however Jha fails to teach the transformation function and activation function as claimed.
Yellapragada (US 2018/0032804) teaches performing OCR on a recognized template document based on hash functions. See Abstract. However, Yellapragada fails to teach the transformation and activation functions as claimed.
Claims 2-7 depend from claim 1 and are therefore also allowed.
Regarding claim 7, neither the closest known prior art, nor any reasonable combination thereof, teaches:
determine a plurality of encoding values, wherein each encoding value of the plurality of encoding values corresponds to a respective symbol of the plurality of symbols;
generate, based on the plurality of encoding values, a vector array comprising a set of hashed symbol values;
apply a predefined transformation to each value of the set of hashed symbol values of the vector array; and
apply an activation function to the vector array to obtain a plurality of feature values associated with the plurality of symbols.
Jha (US 2022/0114644) teaches the use of a hash encoder to determine a sparse representation of an input. See [0045]. Jha uses a plurality of hash encoders to create predictions, however Jha fails to teach the transformation function and activation function as claimed.
Yellapragada (US 2018/0032804) teaches performing OCR on a recognized template document based on hash functions. See Abstract. However, Yellapragada fails to teach the transformation and activation functions as claimed.
Claims 9-14 depend from claim 7 and are therefore also allowed.
Regarding claim 15, neither the closest known prior art, nor any reasonable combination thereof, teaches:
determine a plurality of encoding values, wherein each encoding value of the plurality of encoding values corresponds to a respective symbol of the plurality of symbols;
generate, based on the plurality of encoding values, a vector array comprising a set of hashed symbol values;
apply a predefined transformation to each value of the set of hashed symbol values of the vector array; and
apply an activation function to the vector array to obtain a plurality of feature values associated with the plurality of symbols.
Jha (US 2022/0114644) teaches the use of a hash encoder to determine a sparse representation of an input. See [0045]. Jha uses a plurality of hash encoders to create predictions, however Jha fails to teach the transformation function and activation function as claimed.
Yellapragada (US 2018/0032804) teaches performing OCR on a recognized template document based on hash functions. See Abstract. However, Yellapragada fails to teach the transformation and activation functions as claimed.
Claims 16-20 depend from claim 15 and are therefore also allowed.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. Refer to PTO-892, Notice of References Cited for a listing of analogous art.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Molly K Wilburn whose telephone number is (571)272-3589. The examiner can normally be reached Monday-Friday 8am-4pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Emily Terrell can be reached at (571) 270-3717. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Molly Wilburn/Primary Examiner, Art Unit 2666
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