DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application is being examined under the pre-AIA first to invent provisions.
This office action is in response to the Request for Continued Examination filed 15 October 2025.
This office action is made Non Final.
Claims 1, 9, 12, 17, and 20 have been amended.
The art rejections from the previous office action have been withdrawn as necessitated by the amendment.
Claims 1-5, 9-20 are pending. Claims 1, 9, 12, 17, and 20 are independent claims.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 10/15/25 has been entered.
Specification
After reevaluation of the accepted Abstract filed on 4/22/22, the abstract of the disclosure is objected to because the abstract involves language that is not particularly in narrative form since it repeats the language/wording/phrasing(s) of the independent claims and/or written like a claim. The abstract should be a summary of the claim invention that allows the Office and the public to quickly determine, from a cursory inspection, the nature and gist of the technical disclosure. The abstract should be a summary of the claim invention; not a repeat of the exact/similar wording that is written/used in the independent claims. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
Applicant is reminded of the proper language and format for an abstract of the disclosure.
The abstract should be in narrative form and generally limited to a single paragraph on a separate sheet within the range of 50 to 150 words in length. The abstract should describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text for details.
The language should be clear and concise and should not repeat information given in the title. It should avoid using phrases which can be implied, such as, “The disclosure concerns,” “The disclosure defined by this invention,” “The disclosure describes,” etc. In addition, the form and legal phraseology often used in patent claims, such as “means” and “said,” should be avoided.
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.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
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:
“means to store a component collection” in claim 17;
“means to process process-executable instructions from the component collection” in claim 17.
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.
Claim Rejections - 35 USC § 112
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 1-5, 9-16, 20 are and 17-19 remain 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 1 recites the “processed data tags and data field values” in the extracting and providing steps. However, the previous “processing” limitation already introduced the processing/the processed of the data tags and data field values. Therefore, it is unclear to the Examiner if the element “processed data tags and data field values” should depend on “processing the data field tags and the data field values” of the previous limitation or viewed as its own element. Therefore, the claim is vague and indefinite. For examining purposes, the Examiner will view this portion of limitation as “extracting the processed data tags and data field values; providing the processed data tags and data field values…”
Claims 17 and 20 recite this similar issue as in Claim 1 and are rejected under similar rationale.
Claim 1 recites the limitation "the form fields" in populating step. There is insufficient antecedent basis for this limitation in the claim. For examining purposes, the Examiner will view the limitation of Claim 1 as “…is populated into at least one form field and the at least form field is editable from within a web browser”.
Claims 9, 17, and 20 recite the similar issue and are rejected under similar rationale.
Claim 2 recites the “a confidence review feedback interface” in the receiving step. However, the “compositing” limitation of claim 1 already introduced the confidence review feedback interface element. Therefore, it is unclear to the Examiner if the element “confidence review feedback interface” should depend on “confidence review feedback interface” of the compositing limitation of claim 1 or viewed as its own element. Therefore, the claim is vague and indefinite. For examining purposes, the Examiner will view this portion of limitation as “receiving feedback, via crowd sourced learning engine, from the confidence review feedback interface …”
Claim 3 recites the limitation " the world wide web" in crawling step. There is insufficient antecedent basis for this limitation in the claim. For examining purposes, the Examiner will view the limitation of Claim 3 as “crawling a world wide web”.
Claims 14, and 19 recite the similar issue and are rejected under similar rationale.
Claim 9 recites the “processed data fields” in the extracting step. However, the previous “processing” limitation already introduced the processing data fields. Therefore, it is unclear to the Examiner if the element “processed data fields” should depend on “processing the data fields” of the extracting limitation or viewed as its own element. Therefore, the claim is vague and indefinite. For examining purposes, the Examiner will view this portion of limitation as “extracting the processed data fields…”
Claim 9 recites the limitation " the machine learning confidence information extraction feature" in populating step. There is insufficient antecedent basis for this limitation in the claim. For examining purposes, the Examiner will view the limitation of Claim 9 as “…based on machine learning confidence information from a machine learning confidence information extraction feature”.
Claim 9 recites the limitation " the unknown structured data" in composite step. There is insufficient antecedent basis for this limitation in the claim. For examining purposes, the Examiner will view the limitation of Claim 9 as “composite unknown structured data…”.
Claim 9 recites the limitation " the confidence structured output document web form template" in composite step. There is insufficient antecedent basis for this limitation in the claim. For examining purposes, the Examiner will view the limitation of Claim 9 as “composite … the web form template …”.
As per independent claim 17, the claim limitations containing "“means to store a component collection” and “means to process process-executable instructions from the component collection”” are limitations that invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for the claimed function of each limitation "...means to". Also, no clear algorithm is shown in the specification to correspond to each of the claimed means. This is required as described in MPEP 2181 II.B.
Any claim not specifically addressed, above, is being rejected as its failure to overcome the incorporated deficiencies of a claim upon which is depends on.
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 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.
Claims 1-2, 9, 12-13, 17-18 and 20 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Kizhakkekalathil (US 7882046, 2011) in further view of Scott (US 20040093200, 2004) in further view of Zhu et al (US20080301094 in view of Agrawal (US20110246216, 2011). Tsypliaev et al, US 8478766, filed 2011 is cited as evidence regarding separate tables in relational databases.
As per independent claim 1, Kizhakkekalathil teaches a method comprising:
Including processing Instructions via a processor (Col 23, lines 32-36) ; a memory and a storage (FIG 9), the storage having instructions (Col 23, lines 32-36)
receiving an unknown unstructured data within a structured document (Col. 21 lines 25-40 scans document, and assigns a template to the page), in which the unknown unstructured data within the structured document includes data values without learned pairing to data tags (Col. 21, lines 25-36 labels keywords and concepts of the page using a template to understand a webpage. Furthermore, Applicant’s specification does not define what unstructured data is; therefore, under the broadest reasonable interpretation, unstructured data is viewed as merely text.);
receiving a machine learning confidence information extraction feature; instantiating the received machine learning confidence information extraction feature (Applicant’s specification does not define what instantiating the received…extraction feature is; therefore, the broadest reasonable interpretation within the scope of the relevant art is applied. Col 25, lines 14-54: Fig. 13 collect training data 1306. 1306 is part of procedure of machine learning procedure to create/derive a ML model (such as a Naïve Bayesian model). In addition, training data collected may compile a learning set 408 from the collected transaction data and other data (such as information extracted from the ad opportunity signature. This extraction reference refers to the extracting that occurs during the text mining in Col. 21 lines 25-45. Furthermore, in order for the machine learning procedure to create a ML model from the collected training data and/or to compile ad learning set using the collected training data, the machine learning procedure for extracting has to be instantiated.)
in which the machine learning confidence information extraction feature is obtained from a processing rule database structured including updated machine training data from a feedback user interface; in which the machine learning confidence information extraction feature is from a query of the processing rules database based on the unknown unstructured data;
(Col 25, lines 32-40: training data is collected from data stores (processing rule database structured) relevant to the behavior of users in interacting with ad information. This includes collecting information that identifies what actions the users may have taken in response to the ad presentation opportunities. (Col 24, lines 39-47) One of a skilled artisan would have realized in order to have obtained stored data from a data store (e.g. database), one would have to request the data from the stored location such as a querying the database for the particular data. Ads are presented to the user and in response to the user’s actions of the ads (i.e. clicking) (feedback user interface), behavior information is modified to reflect the user action. This new behavior information is stored in the data store (FIG 1: 126, 122; Col 8, lines 41-49) This new behavior information is also used to update the model (Col 26, lines 4-10))
parsing, via the instantiated machine learning confidence information extraction feature, the unknown unstructured data within the structured document retrieving data field tags and data field values (Col. 21 lines 25-45 applies a template to identify keywords and concepts of the webpage. Furthermore, Col 12, lines 23-24 discloses content-related information from the webpage can be extracted that identifies the nature of text. Col 21, lines 25-45 discloses text mining functionality being performed on content of a web page. Text mining is a form of extracting and text mining results in text being extracted. Furthermore, the text, that is mined, is a web page element. By definition, each webpage element in the webpage has a value. The value of the webpage element, when its text, is the actual text itself. Therefore, when the webpage element is text being mined, a text value is being extracted. Furthermore, by definition, each web page element have or are associated with web page tags. Since the text is in a webpage and is a webpage element, the text is at least associated with a tag. Thus, when the webpage text is being mined/extracted, the tag associated with the webpage element/text is also extracted. Thus, Kizhakkekalathil discloses tags associated with the webpage text and the value of the webpage text is retrieved. Thus, for the extraction functionality to occur, the webpage elements in the page had to be parsed in order for the extraction to occur.)
processing the data field tags and the data field values with the instantiated machine learning confidence information extraction feature (Col. 21 lines 25-45 applies a template to the webpage to process it and generate an advertisement. Furthermore, as explained, Kizhakkekalathil discloses tags associated with the webpage text and the value of the webpage text is retrieved via extraction. During the extraction process, the webpage elements in the webpage had to be identified prior to extracting during the parsing state. Therefore, when the text mining functionality identifies the webpage element (text) on the web page to be extracted, it will also identify the tags associated with the identified element and the value of the element. Thus, the identifying includes processing the identified elements which results in the extraction functionality occurring on the identified elements. )
extracting processed data field tags and data field values (Col. 21 lines 25-45 applies a template to the webpage to process it and generate an advertisement);
providing processed data field tags and data field values to a confidence structured output document learning engine (Fig. 13, 1306 Form learning set from training data);
retrieving a confidence structured output document web form template, in which the confidence structured output document web form template is an HTML format document and includes at least one form field structured for filling out (Fig. 2 webpage template with webpage ad slots (Col 16, ll 39-41: structured to be filled in/out)) ;
populating the confidence structured output document web form template with the extracted data field tags and data field values (populates), based on machine learning confidence information from the instantiated machine learning confidence information extraction feature (Fig. 2 based on machine learning ), generating a confidence structured output document (Col. 25 line 1- Col 29 line 30 processes tags and tag values to produce an advertisement),
compositing the unknown unstructured data with the confidence structured output document web form template into a confidence review feedback user interface (Col. 27 lines 25-65, Fig. 15, 1508, Fig. 13 1314 gathers new data from user to refine the webpage);
providing the confidence review feedback interface (Col. 27 lines 25-65, Fig. 15, 1508, Fig. 13 1314 redefines webpage and learning based on user feedback).
As stated, Kizhakkekalathil discloses collecting/obtaining training data from data stores/databases for training a machine learning model; however, fails to specifically disclose generating a new machine learning confidence extraction feature by employing unknown unstructured training data as machine learning training data, in which the new machine learning confidence extraction feature is discerned as a new variable category of a type of structured data. However, Scott discloses generating a new machine learning confidence extraction feature by employing unknown unstructured training data as machine learning training data, in which the new machine learning confidence extraction feature is discerned as a new variable category of a type of structured data. (0012, 0020: disclose obtaining unstructured/raw text from an input and using it as training data. This training data is used to train a lexical profile for a target category (e.g. create an algorithm to label text with this particular category if the text meets the criteria of the profile). (Abstract, 0012) Once trained, the lexical profile is used to categorized the unstructured/raw text for that category. (0015, 0022, 0027-0028) Since the lexical profile is created, then it’s considered a as a new variable category for categorizing raw text into new type of structure data. Furthermore, Scott discloses a machine learning model since it describes a real-time system that uses the lexical profile as the basis for making confidence judgments (features of a typical machine learning model) for each new incoming message from the same input stream with respect to whether the message is an instance of the target category.
It would have been obvious to one of ordinary skill in the art at the time of Applicant’s invention to have modified Kizhakkekalathil with the disclosed cited features of Scott since it would have provided the intrinsic advantage need for an automated system with a more efficient method for unstructured text categorization by recognizing concepts in unstructured raw text.
Furthermore, Kizhakkekalathil and the cited art fails to specifically disclose in which the new machine learning confidence extraction feature is stored in a separate table for rules in a processing rules database; in which the processing rules database is a relational database. (Note: It is unclear to the Examiner what the language “separate table for rules” is saying other than the “…feature” is being “stored in a table” in a database. The language does not define what table is or how exactly the table is for rules. In addition, the language fails to define what the rules are. The language only mentions the term. In addition, “for rules” is only claimed as intended use and not claimed how the rules are used for the table. Therefore, the broadest reasonable interpretation is applied.)
However, Zhu et al discloses in which the new machine learning confidence extraction feature is stored in a separate table for rules in a processing rules database; in which the processing rules database is a relational database. (0007, 0078, 0130: obtaining generated structured data and storing the structured data into relational database. The structured data is transformed from raw unstructured data and is organized by a category type so it can be stored in the relational database. Furthermore, the Examiner provides Tsypliaev et al, US 8478766, filed 2011, that states relational databases store the information about the various entities in separate tables (Col 6, ll 54-55. In other words, one of a skill artisan would have realized that each entity in a relational database has its own table. Therefore, in 0078, Zhu discloses “a set of data values or numbers that relate to entities of interest to a user are organized into a first structure designated for numbers”. Based on the extrinsic evidence provided by Tsypliaeve et al, the organized first structure, disclosed in Zhu et al, would have been stored in its own table.)
It would have been obvious to one of ordinary skill in the art at the time of Applicant’s invention to have modified the cited art with the disclosed cited features of Zhu et al since it would have provided the benefit of allowing the user(s) with access to the project/folder structure to organize documents and data in their own way (0136)
Furthermore, Kizhakkekalathil fails to disclose a component collection stored in a memory/storage of the component collection and does not appear to teach wherein at least one of the data field values is populated into at least one of the form fields and the form fields are editable from within a web browser. However, Agrawal teaches: wherein at least one of the data field values is populated into at least one of the form fields and the form fields are editable from within a web browser; ( [0020] teaches of using a web browser to interact with a pre-registration form 106 that fills in forms automatically Fig. 4) Furthermore, Agrawal teaches a component collection stored in a memory. (0024 discloses multiple components stored in a memory. FIG 1 shows these components described in 0024 be a part of memory (i.e. stored in memory; thus storage) Furthermore, 0035 discloses the components are software modules of computer-executable instructions residing in a memory (such as memories 124 or 154) and are being executed, as needed, by a processor (such as processors 122 or 152).
It would have been obvious to one of ordinary skill having the teachings of the cited art and Agrawal before him at the time the invention was to modify the cited prior art to include filling in content automatically as taught by Agrawal for a predictable result of increasing the ease of use of entering content for a user.
As per dependent claim 2, Kizhakkekalathil teaches the method of claim 1, further comprising: receiving feedback, via crowd sourced learning engine, from the confidence review feedback interface, in which the feedback includes a correction to at least one of the extracted processed data field tags or data field values; and updating the crowd sourced learning engine based on the received feedback (Col. 27 lines 25-65, Fig. 15, 1508, Fig. 13 1314 redefines webpage and learning based on user feedback).
As per independent Claim 9, Claim 9 is similar to claim 1 and rejected for similar reasons except for: Kizhakkekalathil teaches these additional limitations:
highlighting a discerned document part; receiving a correction on the highlighted document part; updating the initial annotation data set with the correction to generate a new annotation data set; generating a machine learning model based on the received correction; and storing the new annotation data set and the machine learning model (Col. 27 lines 25-65, Fig. 15, 1508, Fig. 13 1314 redefines webpage and learning based on user feedback)
As per independent claims 12, 17, and 20, Claims 12, 17 and 20 recite similar limitations as in Claim 1 and are rejected under similar rationale.
As per dependent claims 13 and 18, Claims 13 and 18 recite similar limitations as in Claim 2 and are rejected under similar rationale.
Claims 3, 14 and 19 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Kizhakkekalathil in further view of Scott in further view of Zhu et al in view of Agrawal in view of Jian (CN 102662954, 2012)
As per dependent claim 3, the cited art does not appear to teach crawling the world wide web for structured documents in a similar subject matter of the unknown unstructured data within the structured document; parsing the structured documents generating a confidence structured output document learning engine feedback from a crowd source, in which the feedback includes a correction to at least one of the extracted processed data field tags or data field values; and updating the confidence structured output document learning engine based on the feedback.
However Jian teaches:
crawling the world wide web for structured documents in a similar subject matter9 of the unknown structured document (Jian: [Abstract] teaches of crawling webpages of a particular field); 10
parsing feedback via the structured documents (web crawlers index webpages, indexing means collects parses and stores data (feedback)) generating a confidence structured output 11 document learning engine from a crowd source (Jian: [Abstract] teaches of updating vectors by analyzing content of webpages), in which the feedback includes 12 an correction to at least one of the extracted processed data field tags or data field 13values (Jian: [Abstract] Teaches of crawling a website to update a machine learning engines vectors, in order to update a topic); and 14
updating the confidence structured output document learning engine based on 15the feedback (Jian: [Abstract] Teaches of crawling a website to update a machine learning engines vectors).
It would have been obvious to one of ordinary skill having the teachings of the cited prior art and Jian before him at the time the invention was to modify the cited prior art to include crawling the web to provide an update to machine learning as taught by Jian for a better result of correcting a document dynamically.
As per dependent claims 14 and 19, Claims 14 and 19 recite similar limitations as in Claim 3 and are rejected under similar rationale.
Claim 4-5 and 15-16 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Kizhakkekalathil in further view of Scott in further view of Zhu et al in view of Agrawal in view of University of Delaware in view of J Real Estate Flyers, November 2007, hereinafter, “Real Estate Flyers”.
As per dependent claim 4, the cited prior art doesn’t teach the unknown unstructured data within the structured document is a real estate property flyer. However, Real Estate Flyers teaches the unknown inconsistent structured 18 document is a real estate property flyer (Real Estate Flyers: Page 6 teaches of a real estate flyer) ..
It would have been obvious to one of ordinary skill having the teachings of the cited prior art and Real Estate Flyers before him at the time the invention was to modify the cited prior art to include real estate flyers with different fields as taught by Real Estate Flyers for a predictable of applying the scanning system to real estate flyers so that the different sections can be identified.
As per dependent claim 5, based on the rejection of Claim 4 and the rationale incorporated, Real Estate Flyers, e.g. page 6, teaches in which the data field tags include a property type (foreclosure), a listing type (property), a street address (7 Magnellana), a city address (Santa maria), a state address (California), a property value ($365,557), a broker name (John Doe), a broker company (K Charles real estate), and a broker contact method (“email@gmail.com”).
As per dependent claims 15 and 16, Claims 15 and 16 recite similar limitations as in Claims 4-5 and are rejected under similar rationale.
Claim 10 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Kizhakkekalathil in further view of Scott in further view of Zhu et al in view of Agrawal in view of Annic (US 20070055489, 2007)
As per dependent claim 10, the cited prior art doesn’t teach the populated web form template with the extracted data fields is provided to multiple crowd-source entities. However, Annic teaches wherein the populated web form template with the extracted data fields is provided to multiple crowd-sourced entities. ([0024] teaches of providing a file to a plurality of users)
It would have been obvious to one of ordinary skill having the teachings of the cited prior art and Annic before him at the time the invention was to modify the cited prior art to include lease license agreements as taught by Annic for a predictable of quickly allowing a lease to be filled out.
Claim 11 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable Kizhakkekalathil in further view of Scott in further view of Zhu et al in view of Agrawal in view of Cath (US 20130179170, 2013).
As per dependent claim 11, the cited prior art doesn’t teach the received correction on the highlighted document type is obtained from multiple crowd-sourced entities. However Cath teaches wherein the received correction on the highlighted document type is obtained from multiple crowd-sourced entities (Cath: [claim 17] teaches of receiving corrections from a plurality of user computers).
It would have been obvious to one of ordinary skill having the teachings of the cited prior art and Cath before him at the time the invention was to modify the cited prior art to corrections from a plurality of user computers as taught by Cath for allowing for the system to be improved from a large range of audiences.
Response to Arguments
Applicant’s arguments with respect to claims 1-5, 9-20 have been considered but are moot because the arguments do not apply to the new ground(s) of rejection(s) since the new ground(s) of rejection(s) was necessitated by Applicant's amendment.
Conclusion
If the Applicant chooses to amend the claims in future filings, the Examiner kindly states any new limitation(s) added to the claims must be described in the specification in such a way as to reasonably convey to one skilled in the relevant art in order to meet the written description requirement of 35 USC 112, first paragraph. To help expedite prosecution, promote compact prosecution and prevent a possible 112(a)/first paragraph rejection, the Examiner respectfully requests for each new limitation added to the claims in a future filing by the Applicant that the Applicant would cite the location within the specification showing support for that new limitation within the remarks. In addition, MPEP 2163.04(I)(B) states that a prima facie under 112(a)/first paragraph may be established if a claim has been added or amended, the support for the added limitation is not apparent, and applicant has not pointed out where added the limitation is supported.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVID FABER whose telephone number is (571)272-2751. The examiner can normally be reached Monday - Thursday.
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, Adam Queler can be reached at 5712724140. 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.
/ADAM M QUELER/ Supervisory Patent Examiner, Art Unit 2172
/D.F/ Examiner, Art Unit 2172