DETAILED ACTION
This action is responsive to application filed on October 26, 2023.
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 .
Information Disclosure Statement
As required by M.P.E.P. 609, the applicant’s submission of the Information Disclosure Statement dated October 26, 2023 is acknowledged by the examiner and the cited references have been considered in the examination of the claims now pending.
Claim Objections
Claims 6, 12 and 16 are objected to because of the following informalities:
In claims 6 and 16: “the authorship” lacks proper antecedent basis.
In claims 12 “the interface” lacks proper antecedent basis.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 10 and 20 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. because the specification, while being enabling for certain OCR and NLP processing techniques, does not reasonably provide adequate written description support for the full scope of “wherein the one or more suggestions are generated using Generative Al.”
The specification fails to adequate describe: What constitutes the claimed “Generative AI”? What’s the architecture of the generative model? How the claimed generative suggestions are produced? How the generative model is trained?
Accordingly, 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.
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 1-20 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.
Claims 1 and 11 recite “identify one or more keywords… and making the one or more keywords available in real-time;” It is unclear what “making” the one or more keywords “available” refers to. Does refers to displaying the keywords to a user? Does it refers to transmitting the keyword to another process? Does it refers to storing the keywords in memory? Making the keywords retrievable? Examiner requests clarification from Applicant as the specification does not provide sufficient support to discern the metes and bounds of the claim. The specification fails to provide objective boundaries for determining when information has been made available. The metes and bounds of “making the one or more keywords available in real-time” is unclear and thus the scope of the claim is indefinite.
Claims 1 and 11 further recite “making the one or more suggestions in real-time;” and “making the relationships available in real-time.” Similarly, it is unclear what “making” the suggestions / relationships “available” refers to. Does refers to displaying suggestions / relationships to a user? Does it refers to transmitting the suggestions/relationships to another process? Does it refers to storing the suggestions / relationships in memory? Making the suggestions / relationships retrievable? Examiner requests clarification from Applicant as the specification does not provide sufficient support to discern the metes and bounds of the claim. The specification fails to provide objective boundaries for determining when information has been made available. The metes and bounds of “making the one or more suggestions in real-time;” and “making the relationships available in real-time.” is unclear and thus the scope of the claim is indefinite.
Further, the limitation “in real-time” is unclear whether it refers to the OCR processing, NLP processing, (keyword identification, suggestion identification, relationship identification), presenting to a user, or storing results. Additionally, because the claim do not require the use of the keywords, suggestions or relationships after they are “made available” it is unclear whether the limitation imposes any meaningful operational constraint on the claimed invention, therefore it is indefinite.
Claims 4 and 14 recite “making the authorship available in real-time”. Similar to rejected claim 1 above this limitation is unclear and renders the claim indefinite. The specification fails to provide objective boundaries for determining when information has been made available. The metes and bounds of “making the one or more suggestions in real-time;” and “making the relationships available in real-time.” is unclear and thus the scope of the claim is indefinite.
Claims 7-8 (depend on claim 1) and 17-18 (depend on claim 11) recite “the writing sample”. The term “the writing sample” lacks proper antecedent basis. Also, it is unclear whether “the writing sample” refers to handwritten notes collected from the collaboration session, or does it refer to the OCR generated text strings? Does it refer to authorship data or historical handwriting information? The metes and bounds of “the writing sample” is unclear and thus the scope of the claim is indefinite.
Dependent claims 2-10 and 12-20 are also rejected based on their dependency to the above rejected claims.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention recites a judicial exception, is directed to that judicial exception, an abstract idea, as it has not been integrated into practical application and the claims further do not recite significantly more than the judicial exception. Examiner has evaluated the claims under the framework provided in the 2024 USTO Guidance Update on Patent Subject Matter Eligibility (“2024 AI SME Update”).
Step 1 analysis:
In the instant case, the claims are directed to a system (claims 1-10) and method (claims 11-20). Thus, each of the claims falls within one of the four statutory categories (i.e. process, machine, manufacture of composition of matter).
Step2A analysis:
Based on determining the claim fall within or can be amended to fall within a statutory category (Step 1), it must be determined if the claims are directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), in this case the claims fall within the judicial exception of an abstract idea. Specifically, the abstract idea of mental processes.
Step 2A: Prong 1 analysis:
The claim(s) recite(s):
Claim 1 (Similarly in claim 11) :
“applying computer vision processing to parse an image displayed on the digital touch interface into a set of objects that comprise a set of handwritten notes;” (Mental process – identifying handwritten notes can be practically performed mentally [e.g. evaluation and observation].)
“performing optical character recognition processing on the set of handwritten notes to identify a corresponding set of text strings;” (Mental process - identifying text can be practically performed mentally [e.g. observation].)
“applying natural language processing to identify one or more keywords based on the set of text strings and making the one or more keywords available in real-time;” (Mental process – keyword identification from text can be practically performed mentally [e.g. observation])
“applying a recommender engine to identify one or more suggestions that support the set of text strings and making the one or more suggestions in real-time;” (Mental process - identifying suggestions associated with text can be practically performed mentally [e.g. observation, evaluation, opinion and judgment].)
“applying a classifier to identify relationships between the set of text strings and making the relationships available in real-time.” (Mental process - identifying groupings related to text can be practically performed mentally [e.g. observation, evaluation, opinion and judgment].)
Step 2A: Prong 2 analysis:
This judicial exceptions are not integrated into a practical application. In particular the recitation of “interface”, “digital touch interface”, “computer processor”, “OCR processing”, NLP processing”, “classifier” and “recommender engine” In claim 1 and similarly in claim 11, are instruction to apply the abstract idea using a generic computer. Accordingly, these additional elements do not integrate the abstract idea into a practical application because the claim amount to nothing more than an instruction to apply the abstract idea using a generic computer. The claim is directed to an abstract idea.
Step 2B analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using an “interface”, “digital touch interface”, “computer processor”, “OCR processing”, NLP processing”, “classifier” and “recommender engine”, amount to no more than mere instructions to apply the exception using a system. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Dependent claim(s) 2-10 and 12-20 when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea, as they recite further embellishment of the judicial exception.
Claim 2 (Similarly in claim 12):
“wherein the interface communicates with a second digital touch interface at a second location.” (Insignificant extra-solution activity - WURC in accordance with MPEP 2106.05(d) - Receiving or transmitting data over a network)
Further claims 2 and 12 do not recite any other additional elements and for the same reasons as above with regard to integration into practical application and whether additional elements amount to significantly more, claims 2 and 12 also fail both Step 2A prong 2, thus the claims are directed to the judicial exception as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, Claims 2 and 12 do not recite patent eligible subject matter under 35 U.S.C. § 101.
Claim 3 (Similarly in claim 13):
“applying the natural language processing to correct errors wherein the errors comprise typographical errors and punctuation; and” (mental process - correcting typographical errors and punctuation can be performed mentally or with the aid of pen and paper [e.g. observation, evaluation and judgment].)
“applying the natural language processing to create a word cloud graphic based on the corresponding set of text strings wherein the word cloud graphic is based on a metric.” (Mental process- create a word visualization based on text can be practically performed mentally with the aid of pen and paper [e.g. observation, evaluation and judgment].)
Further claims 3 and 13 do not recite any other additional elements and for the same reasons as above with regard to integration into practical application and whether additional elements amount to significantly more, claims 3 and 13 also fail both Step 2A prong 2, thus the claims are directed to the judicial exception as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, Claims 3 and 13 do not recite patent eligible subject matter under 35 U.S.C. § 101.
Claims 4 and 6 (Similarly in claims 14 and 16):
“applying the natural language processing to attribute authorship to each of the text strings based on a writing sample and making the authorship available in real-time.” (Mental process- identifying the creator of a text based writing sample can be performed mentally or with the aid of pen and paper [e.g. observation, evaluation and judgment].)
Further claims 4, 6, 14 and 16 do not recite any other additional elements and for the same reasons as above with regard to integration into practical application and whether additional elements amount to significantly more, claims 4, 6, 14 and 16 also fail both Step 2A prong 2, thus the claims are directed to the judicial exception as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, Claims 4, 6, 14 and 16 do not recite patent eligible subject matter under 35 U.S.C. § 101.
Claim 5 (Similarly in claim 15):
“wherein the natural language processing is applied to identify a company business theme in real-time.” (Mental process- identifying a topic based on text can be performed mentally [e.g. observation, evaluation and judgment].)
Further claims 5 and 15 do not recite any other additional elements and for the same reasons as above with regard to integration into practical application and whether additional elements amount to significantly more, claims 5 and 15 also fail both Step 2A prong 2, thus the claims are directed to the judicial exception as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, Claims 5 and 15 do not recite patent eligible subject matter under 35 U.S.C. § 101.
Claim 7 (Similarly in claim 17):
“wherein the writing sample is predetermined.” (Insignificant extra-solution activity MPEP 2106.05(g) – Selecting a particular data source or type of data to be manipulated.)
Further claims 7 and 17 do not recite any other additional elements and for the same reasons as above with regard to integration into practical application and whether additional elements amount to significantly more, claims 7 and 17 also fail both Step 2A prong 2, thus the claims are directed to the judicial exception as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, Claims 7 and 17 do not recite patent eligible subject matter under 35 U.S.C. § 101.
Claim 8 (Similarly in claim 18):
“the writing sample is captured during a current collaboration session in real-time.” (Insignificant extra-solution activity MPEP 2106.05(g) – Selecting a particular data source or type of data to be manipulated.)
Further claims 8 and 18 do not recite any other additional elements and for the same reasons as above with regard to integration into practical application and whether additional elements amount to significantly more, claims 8 and 18 also fail both Step 2A prong 2, thus the claims are directed to the judicial exception as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, Claims 8 and 18 do not recite patent eligible subject matter under 35 U.S.C. § 101.
Claim 9 (Similarly in claim 19):
“wherein the one or more suggestions comprise one or more action items.” (Insignificant extra-solution activity MPEP 2106.05(g) – Selecting a particular data source or type of data to be manipulated.)
Further claims 9 and 19 do not recite any other additional elements and for the same reasons as above with regard to integration into practical application and whether additional elements amount to significantly more, claims 9 and 19 also fail both Step 2A prong 2, thus the claims are directed to the judicial exception as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, Claims 9 and 19 do not recite patent eligible subject matter under 35 U.S.C. § 101.
Claim 10 (Similarly in claim 20):
“wherein the one or more suggestions are generated using Generative Al.” (merely indicates a field of use or technological environment in which the judicial exception is performed. See MPEP 2106.05(h).)
Further claims 10 and 20 do not recite any other additional elements and for the same reasons as above with regard to integration into practical application and whether additional elements amount to significantly more, claims 10 and 20 also fail both Step 2A prong 2, thus the claims are directed to the judicial exception as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, Claims 10 and 20 do not recite patent eligible subject matter under 35 U.S.C. § 101.
The claims are not patent eligible.
Viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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.
Claims 1-2, 5, 8-9, 11-12, 15 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Biller (US Patent Publication No. US 9070036 B2), in view of Ghatage (US Patent Application Publication No. US 20190236173 A1), in view of Moorjani (US Patent Publication No. US 10613825 B2 – hereinafter Reference3).
Regarding claim 1, Biller teaches a computer-implemented system that digitizes and mines handwritten notes to support real-time collaboration, the system comprising: an interface that communicates with a digital touch interface at a location during a collaboration session; and a computer processor that is connected to the interface and further programmed to perform the steps of: (See Biller abstract “At least some aspects of the present disclosure feature systems and methods for note recognition [Thus, a system that digitizes and mines handwritten notes]. The note recognition system includes a sensor, a note recognition module, and a note extraction module.” See also Biller Col. 1, lines 12-15, Col 3, lines 6-34“during a collaboration session (e.g., brainstorming session), participants write down ideas on Post-It® notes, whiteboard, or paper, and then share with one another… notes are used in a collaboration space. Collaboration space generally refers to a gathering area [Thus, at a location during a collaboration session] allowing more than one person to share ideas and thoughts with each other [Thus, to support real-time collaboration]… Digital notes can be generated using digital inputs. Digital inputs can include… touch screens [i.e. digital touch interface]… In the example of FIG. 1A, environment 10 includes a mobile device 15 to capture and recognize one of more notes 22 from a workspace 20… notes 22 may be the results of a collaborative brainstorming session having multiple participants… mobile device 15… may perform a variety of note-related operations, including automated creation of digital notes representative of physical notes 22… mobile device 15 may include one or more processors, microprocessors” See also Biller Col. 4, lines 61-63 “mobile device 15 stores the image data in data storage 68 for access and processing by note management application 78 and/or other user applications 77”)
applying computer vision processing to parse an image displayed on the digital touch interface into a set of objects that comprise a set of handwritten notes; (See Biller claim 1 “capturing, by a sensor of a computing device, an image, comprising a visual representation of a scene having a plurality of physical notes, each of the physical notes comprising a separate physical object having a predefined boundary and recognizable content thereon; processing, by a processing unit of the computing device, image data associated with the image to identify a predefined boundary [Thus, applying computer vision processing] of one of the plurality of physical notes [Thus, into a set of objects that comprise a set of handwritten notes] from the visual representation;” See also Biller Col. 2, lines 59-60 “By way of examples, physical notes can include hand-written Post-It®” See also Biller col. 3, lines 30-57 “mobile device 15 includes, among other components, an image capture device 18 and a presentation device 28… image capture device 18 is a camera or other component configured to capture image data representative of workspace 20 and notes 22… mobile device 15 generates the content to display on presentation device 28 for the notes”)
performing optical character recognition processing on the set of handwritten notes to identify a corresponding set of text strings; (See Biller claim 1 “extracting, by the processing unit and based at least in part on identifying the predefined boundary, from the image data the recognizable content from within the predefined boundary of the one of the plurality of physical notes [i.e. set of handwritten notes] and based at least in part on a contrast between the recognizable content and a background of the one of the plurality of physical notes from the visual representation” See also Biller Col. lines “the system can further recognize text [Thus, identify a corresponding set of text strings] and figures from the extracted content” Thus, Biller extract recognizable content from each detected note.)
Biller lacks details regarding performing optical character recognition processing.
However, Ghatage teaches performing optical character recognition processing on the set of handwritten notes to identify a corresponding set of text strings in more details. (See Ghatage [0011, 0014] “ Some implementations described herein provide an integration platform that utilizes artificial intelligence to automatically integrate data from multiple diverse sources into a unified view of the data (e.g., a data structure)… the integration platform may perform one or more processing techniques on one or more of the data files in order to convert the one or more data files into an electronic, or machine-encoded, data (e.g., processed data files)… the integration platform may utilize optical character recognition (OCR) with the one or more data files in order to convert the one or more data files into electronic data files. Optical character recognition involves a conversion of images of typed, handwritten [e.g. handwritten notes], or printed text into machine-encoded text. For example, OCR may be applied to… to produce electronic data (e.g., text data) [Thus, identify a corresponding set of text strings]… electronic data allows the information represented by the printed text to be electronically edited, searched… Implementations of OCR may employ pattern recognition, artificial intelligence, computer vision, and/or the like.”)
Both Biller and Ghatage recognize text from images. Biller recognizes and extracts textual content from handwritten note images. Ghatage utilize optical character recognition (OCR) converting images of handwritten text into machine-readable text.
Therefore, a person having ordinary skills in the art would have found it obvious to substitute or supplement Biller’s text-recognition extraction processing with Ghatage’s OCR processing, as both represent known and interchangeable techniques for accessing required data, yielding predictable results and improving system flexibility.
Biller further in view of Ghatage, [hereinafter Biller-Ghatage] additionally disclose applying natural language processing to identify one or more keywords based on the set of text strings and making the one or more keywords available in real-time; (See Ghatage [0016-0017, 0021] “Natural language processing can be applied to analyze text… enabling… topic extraction, named entity recognition, parts-of-speech tagging, relationship extraction… the integration platform may extract data from the processed data files… the integration platform may utilize a natural language processing technique… with the extracted data in order to make the extracted data analyzable… the integration platform may utilize an entity extraction method (e.g., also called an entity identification method… to identify [Thus, identifying one or more keywords] and classify named entities [e.g. keywords] in the extracted data into pre-defined categories [Thus, making the keywords available (e.g. in real-time)], such as names of persons, organizations, locations [Thus, applying natural language processing to identify one or more keywords based on the set of text strings]”
Examiner notes that the term “making the one or more keywords available in real-time” (in the claims and in the specification) is used in a conclusory manner and the claimed applying of the natural language processing and the identification of the keywords steps must occur first, before making the keywords available “in real-time”. Thus, Ghatage’s step of identifying named entities (e.g. keywords) and classifying them constitutes making keywords available in real-time.
applying a recommender engine to identify one or more suggestions that support the set of text strings and making the one or more suggestions in real-time; and
Biller-Ghatage does not explicitly disclose identify one or more suggestions that support the set of text strings.
However, Moorjan teaches applying a recommender engine to identify one or more suggestions that support the set of text strings and making the one or more suggestions in real-time. (See Reference3 col. 1, lines 41-43 “improved techniques are directed to providing electronic text recommendations to a user based on content discussed during a conference” See Reference3 col. 6, lines 41-44 “the processing circuitry 74 operating in accordance with the set of specialized applications [e.g. applying a recommender engine] and data 84 forms specialized control circuitry to render electronic text recommendations [e.g. identify one or more suggestions that support the set of text strings] to a user 30 for use.” See also Moorjan col. 2, lines 66-67, col. 3, lines 1-3, claim 1 “electronically recognizing the text in the visual input includes performing a set of optical character recognition (OCR) operations on the visual input displayed during the conference to electronically recognize the text [e.g. set of text strings]… capturing, by electronic circuitry, content from a conference among multiple participants, at least in part by identifying terms available for recommendation from electronically acquired participant input that includes visual input displayed to the multiple participants during the conference… by performing optical character recognition operations on the visual input displayed during the conference; [Thus, identify one or more suggestions that support the set of text strings and making the one or more suggestions in real-time]” See also Reference3 col. 12, lines 11-13 “it should be understood that the process of providing such recommendations to the user may be performed in real time during a meeting”)
One on ordinary skills in the art would be motivated to incorporate Moorjan’s recommendation system into Biller-Ghatage to automatically generate suggestions responsive to extracted handwritten collaboration content in order to improve the meeting productivity and assist users during collaborative interactions.
Biller-Ghatage further in view of Reference3, [hereinafter Biller-Ghatage-Refrence3] additionally disclose applying a classifier to identify relationships between the set of text strings and making the relationships available in real-time. (See Ghatage [0016-0017, 0021] “Natural language processing can be applied to analyze text… enabling real world applications such as… relationship extraction… the integration platform may utilize… incremental extraction method [that] may detect the changes in the data files… the integration platform may… identify and classify named entities in the extracted data into pre-defined categories” See also Ghatage [0025] “the data import/export model may include … a k-means clustering machine learning model that determines data parameters based on the data [e.g. text strings]. The k-means clustering method may be associated with a k-nearest neighbor classifier method (e.g., a machine learning method for classification), and may apply a one-nearest neighbor classifier on cluster [Thus, identify relationships between the set of text strings] centers obtained by k-means to classify new data [e.g. text strings] into existing clusters. [Thus, applying a classifier to identify relationships between the set of text strings]”)
Regarding claim 2, Biller-Ghatage-Refrence3 teaches all limitations and motivations of claim 1, wherein the interface communicates with a second digital touch interface at a second location. (See Biller Col. 1, lines 12-15, Col 3, lines 6-34 “during a collaboration session (e.g., brainstorming session), participants write down ideas on Post-It® notes, whiteboard, or paper, and then share with one another… notes are used in a collaboration space. Collaboration space generally refers to a gathering area [Thus, at a location during a collaboration session] allowing more than one person to share ideas and thoughts with each other… Digital notes can be generated using digital inputs. Digital inputs can include… touch screens [e.g. second digital touch interface]… the note management system may receive a set of images of a number of notes from a camera or a smart phone and receive another set of images of a number of notes taken from a remote location.”)
Regarding claim 5, Biller-Ghatage-Refrence3 teaches all limitations and motivations of claim 1, wherein the natural language processing is applied to identify a company business theme in real-time. (See Ghatage [0016, 0021] “Natural language processing can be applied to analyze text… enabling real world applications such as automatic text summarization, sentiment analysis, topic extraction, named entity recognition… the integration platform may utilize a natural language processing technique… to identify and classify named entities in the extracted data into pre-defined categories [e.g. themes], such as names of persons, organizations [e.g. company business], locations, expressions of time, quantities, monetary values, percentages, and/or the like.”)
Regarding claim 8, Biller-Ghatage-Refrence3 teaches all limitations and motivations of claim 1, wherein the writing sample is captured during a current collaboration session in real-time. (Biller teaches sharing ideas among users based on handwritten notes during collaboration brainstorming session. See also Biller col. 6, lines 5-7 “the processing unit 110 extracts the content of the note [e.g. writing sample] from the visual representation in real-time”)
Regarding claim 9, Biller-Ghatage-Refrence3 teaches all limitations and motivations of claim 1, wherein the one or more suggestions comprise one or more action items. (See Reference3 col. 6, lines 41-44 “the processing circuitry 74 operating in accordance with the set of specialized applications and data 84 forms specialized control circuitry to render electronic text recommendations [e.g. identify one or more suggestions] to a user 30 for use [e.g. action item].” See also Reference3 “The user 30… simply select the electronic text recommendations 160 [e.g. action item] rather than be burdened by typing out an entire term 122.” Examiner interprets Reference3’s selectable recommendation used to avoid typing a term as an action item.)
Regarding claim 11, Biller-Ghatage-Refrence3 teaches all of the elements of claim 1 in method form rather than system form. Therefore, the supporting rationale of the rejection to claim 1 applies equally as well to those elements of claim 11.
Regarding claim 12, Biller-Ghatage-Refrence3 teaches all of the elements of claim 2 in method form rather than system form. Therefore, the supporting rationale of the rejection to claim 2 applies equally as well to those elements of claim 12.
Regarding claim 15, Biller-Ghatage-Refrence3 teaches all of the elements of claim 5 in method form rather than system form. Therefore, the supporting rationale of the rejection to claim 5 applies equally as well to those elements of claim 15.
Regarding claim 18, Biller-Ghatage-Refrence3 teaches all of the elements of claim 8 in method form rather than system form. Therefore, the supporting rationale of the rejection to claim 8 applies equally as well to those elements of claim 18.
Regarding claim 19, Biller-Ghatage-Refrence3 teaches all of the elements of claim 9 in method form rather than system form. Therefore, the supporting rationale of the rejection to claim 9 applies equally as well to those elements of claim 19.
Claims 3 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Biller-Ghatage-Refrence3 in view of Zes (US Patent Application Publication No. US 20150262401 A1).
Regarding claim 3, Biller-Ghatage-Refrence3 teaches all limitations and motivations of claim 1, wherein the computer processor is further programmed to perform the steps of: applying the natural language processing to correct errors wherein the errors comprise typographical errors and punctuation; and (See Ghatage [0016, 0020] “the integration platform may apply natural language processing (NLP) to interpret the processed data files… the integration platform may utilize a data cleansing method to process the extracted data and to detect and/or correct corrupt or inaccurate data from the extracted data… The data cleansing method may include removing typographical errors [Thus, applying the natural language processing to correct errors wherein the errors comprise typographical errors] or validating and correcting values…The data cleansing method may also involve activities, such as harmonization of data (e.g., harmonization of short codes (e.g., St., Rd., and/or the like [e.g. addressing punctuation]) to actual words (e.g., street, road, and/or the like).”)
Biller-Ghatage-Reference3 does not explicitly disclose create a word cloud.
However, Zes teaches create a word cloud graphic based on the corresponding set of text strings wherein the word cloud graphic is based on a metric. (See Zes [0011-0012] “the multi-dimensional visualization allows the word cloud to quickly convey both (1) a textual description of a particular employee, and (2) a personality factor of a particular employee (for example, if most of the relatively large words have relatively warm colors, then a viewer of the word cloud may readily infer that the employee has a relatively warm personality). FIG. 1 illustrates an exemplary psychometric descriptor word cloud generated according to examples of the disclosure. The word cloud has been generated [Thus, create a word cloud graphic] such that the words [i.e. text strings] fit inside a predetermined polygon having the shape of a gear with the letters A, B, and C cut out of the center. Further, as described above, each of the words have a relative size determined based on a first score and a relative color determined based on a second score [Thus, based on a metric].”)
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify Biller-Ghatage-Reference3 to incorporate the teachings of Zes word cloud visualization technique to extracted collaboration keywords in order to visually summarize dominant meeting themes and improve user understanding of frequently discussed concepts because word clouds are well-known visual analytic tools for summarizing textual datasets.
Regarding claim 13, Biller-Ghatage-Refrence3 in view of Zes teaches all of the elements of claim 3 in method form rather than system form. Therefore, the supporting rationale of the rejection to claim 3 applies equally as well to those elements of claim 13.
Claims 4, 6-7, 14 and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Biller-Ghatage-Refrence3 in view of Carpenter (US Patent Application Publication No. US 20110058742 A1).
Regarding claim 4, Biller-Ghatage-Refrence3 teaches all limitations and motivations of claim 1.
Biller teaches sharing ideas among users based on handwritten notes during collaboration brainstorming session [e.g. in real-time].
Biller-Ghatage-Reference3 does not explicitly disclose attribute authorship to each of the text strings based on a writing sample.
However, Carpenter teaches attribute authorship to each of the text strings based on a writing sample and making the authorship available in real-time. (See Carpenter abstract, claim 1 “Systems, methods, and computer-readable mediums for determining authorship [Thus, attribute authorship] of a handwritten document for which the authorship is not known… content analysis of the document is also performed and used to determine authorship… storing [Thus, making the authorship available (e.g. in real-time)] the stylus information, authorship information, and authorship of the document. [Thus, processing to attribute authorship to each of the text strings]” See also Carpenter [0028] “In order to use the content analysis the text must be available as clear readable text [i.e. text strings]. This means an OCR will be used to read the text completely [Thus, each of the text strings]… for a specific content analysis method, like the statistical measurements of a set of function words.” See also Carpenter [0015, 0032] “The rate and accuracy of the authorship determination may be enhanced by classifying the ink [Thus, predetermined] used in handwriting samples [Thus, based on a writing sample] and references… optical character recognition (OCR) processes are used to “read” the text in the writing sample, and the recognized text [e.g. text strings] is screened against important keywords.”)
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify Biller-Ghatage-Reference3 to incorporate the teachings of Carpenter handwriting attribution techniques to collaborative handwritten notes in order to identify contributors, preserve idea provenance and improve accountability of participant contributions during collaborative meetings.
Regarding claim 6, Biller-Ghatage-Refrence3 in view of Carpenter teaches all of the elements of claim 4 in method form. Therefore, the supporting rationale of the rejection to claim 4 applies equally as well to those elements of claim 6.
Regarding claim 7, Biller-Ghatage-Refrence3 in view of Carpenter teaches all of the elements of claim 4 in method form. Therefore, the supporting rationale of the rejection to claim 4 applies equally as well to those elements of claim 7.
Regarding claim 14, Biller-Ghatage-Refrence3 in view of Carpenter teaches all of the elements of claim 4 in method form rather than system form. Therefore, the supporting rationale of the rejection to claim 4 applies equally as well to those elements of claim 14.
Regarding claim 16, Biller-Ghatage-Refrence3 in view of Carpenter teaches all of the elements of claim 6 in method form rather than system form. Therefore, the supporting rationale of the rejection to claim 6 applies equally as well to those elements of claim 16.
Regarding claim 17, Biller-Ghatage-Refrence3 in view of Carpenter teaches all of the elements of claim 7 in method form rather than system form. Therefore, the supporting rationale of the rejection to claim 7 applies equally as well to those elements of claim 17.
Claims 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Biller-Ghatage-Refrence3 in view of Sarikaya (US Patent Application Publication No. US 20160364382 A1).
Regarding claim 10, Biller-Ghatage-Refrence3 teaches all limitations and motivations of claim 1, wherein the one or more suggestions are generated using Generative Al. (Ghatage teaches artificial intelligence models including deep learning neural network model to generate [Thus, generative] a unified view of the text data. Reference3 teaches generation of suggestion and the use of language model tuned with historical keywords and phrased [e.g. artificial intelligence] (Reference3 Col. 12, lines 64-67).)
Biller-Ghatage-Reference3 does not explicitly recite a generative AI.
However, Sarikaya teaches wherein suggestions are generated using Generative Al in more details. (See Sarikaya abstract describing a contextual language generation using “ a machine learned language prediction model is trained with features extracted from multiple sources, such as log data and session context… a surface form of the suggestion is generated that can be presented to the user.” See also Sarikaya [0003] “ generating and presenting suggestions to users… the trained machine learned language prediction model is used to determine the most likely suggestion to present to the user… The surface form of the suggestion is a grammatical, natural language command, phrase, or sentence that the user can understand”)
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify Biller-Ghatage-Reference3 to incorporate the teachings of Sarikaya machine-learning contextual language generation techniques with Reference3’s recommendation system together with the OCR/NLP semantic understanding techniques of Ghatage in order to improve the contextual relevance of suggestions generated during the collaboration session.
Regarding claim 20, Biller-Ghatage-Refrence3 in view of Sarikaya teaches all of the elements of claim 20 in method form rather than system form. Therefore, the supporting rationale of the rejection to claim 20 applies equally as well to those elements of claim 20.
Conclusion
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/OSCAR WEHOVZ/Examiner, Art Unit 2161
/APU M MOFIZ/Supervisory Patent Examiner, Art Unit 2161
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