Notice of Pre-AIA or AIA Status
1. 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
2. The information disclosure statement (IDS) submitted on January 17, 2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
3. 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.
4. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The independent claim 1 recites “A computer-implemented method, comprising: receiving information comprising a text segment to be inserted into a body of text displayed in a user interface; identifying a context, wherein the context is based at least in part on text surrounding the inserted text segment in the body of text; generating an input that comprises the text segment and the context; processing the input using a trained neural network to generate a suggested modification to the inserted text segment, to the context, or both; and presenting the suggested modification to a user in the user interface”.
The limitations “receiving information comprising a text segment to be inserted into a body of text displayed in a user interface; identifying a context, wherein the context is based at least in part on text surrounding the inserted text segment in the body of text; generating an input that comprises the text segment and the context; processing the input using a trained neural network to generate a suggested modification to the inserted text segment, to the context, or both; and presenting the suggested modification to a user in the user interface” as drafted, covers a mental process, as this could be done by mentally or by hand with pen and paper.
This judicial exception is not integrated into a practical application. Claim 1 recites “A computer-implemented method, comprising:…”. This limitation directs towards using a computer for the method, and does not impose any meaningful limits on practicing the abstract idea.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The addition of the generic computer components recited above with regard to claim 1 do not amount to more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Claim 1 does not recite any additional limitations. The claim as drafted, is not patent eligible.
Claims 2-10 are rejected for their dependence on claims 1, because they do not contain additional limitations that overcome the present rejection.
The independent claim 11 recites “A system comprising one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform the operations comprising: receiving information comprising a text segment to be inserted into a body of text displayed in a user interface; identifying a context, wherein the context is based at least in part on text surrounding the inserted text segment in the body of text; generating an input that comprises the text segment and the context; processing the input using a trained neural network to generate a suggested modification to the inserted text segment, to the context, or both; and presenting the suggested modification to a user in the user interface”.
The limitations “receiving information comprising a text segment to be inserted into a body of text displayed in a user interface; identifying a context, wherein the context is based at least in part on text surrounding the inserted text segment in the body of text; generating an input that comprises the text segment and the context; processing the input using a trained neural network to generate a suggested modification to the inserted text segment, to the context, or both; and presenting the suggested modification to a user in the user interface” as drafted, covers a mental process, as this could be done by mentally or by hand with pen and paper.
This judicial exception is not integrated into a practical application. Claim 1 recites” A system comprising one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform the operations comprising:…”. This limitation directs towards using a computer for the method, and does not impose any meaningful limits on practicing the abstract idea.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The addition of the generic computer components recited above with regard to claim 11 do not amount to more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Claim 11 does not recite any additional limitations. The claim as drafted, is not patent eligible.
Claims 12-15 are rejected for their dependence on claims 11, because they do not contain additional limitations that overcome the present rejection.
The independent claim 16 recites “One or more computer storage media storing instructions that when executed by one or more computers cause the one or more computers to perform operations comprising: receiving information comprising a text segment to be inserted into a body of text displayed in a user interface; identifying a context, wherein the context is based at least in part on text surrounding the inserted text segment in the body of text; generating an input that comprises the text segment and the context; processing the input using a trained neural network to generate a suggested modification to the inserted text segment, to the context, or both; and presenting the suggested modification to a user in the user interface”.
The limitations “receiving information comprising a text segment to be inserted into a body of text displayed in a user interface; identifying a context, wherein the context is based at least in part on text surrounding the inserted text segment in the body of text; generating an input that comprises the text segment and the context; processing the input using a trained neural network to generate a suggested modification to the inserted text segment, to the context, or both; and presenting the suggested modification to a user in the user interface”. as drafted, covers a mental process, as this could be done by mentally or by hand with pen and paper.
This judicial exception is not integrated into a practical application. Claim 16 recites ““One or more computer storage media storing instructions that when executed by one or more computers cause the one or more computers to perform operations comprising:…”. This limitation directs towards using a computer for the method, and does not impose any meaningful limits on practicing the abstract idea.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The addition of the generic computer components recited above with regard to claim 16 do not amount to more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Claim 16 does not recite any additional limitations. The claim as drafted, is not patent eligible.
Claims 17-20 are rejected for their dependence on claims 16, because they do not contain additional limitations that overcome the present rejection.
Claim Rejections - 35 USC § 102
5. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
6. Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Guberman (U.S. Publication No. 20230123574).
Regarding claim 1, Guberman discloses a computer-implemented method, comprising:
receiving information comprising a text segment to be inserted into a body of text displayed in a user interface ([0025] - experts may identify or enter such documents via graphical user interface [0026] - receive user inputted legal text 136 from a user device 140 being operated by a human user 144 to create a user legal document 148, analyze user inputted legal text 136 using natural language processing model 112);
identifying a context, wherein the context is based at least in part on text surrounding the inserted text segment in the body of text ([0072] - Speech tagging or part-of-speech (POS) tagging, sometimes also called grammatical tagging, may be considered to be a process of marking up a word in a text (corpus) as corresponding to a particular part of speech, based on both its definition and its context);
generating an input that comprises the text segment and the context ([0072] - Speech tagging or part-of-speech (POS) tagging, sometimes also called grammatical tagging, may be considered to be a process of marking up a word in a text (corpus) as corresponding to a particular part of speech, based on both its definition and its context);
processing the input using a trained neural network to generate a suggested modification to the inserted text segment, to the context, or both ([0020] - natural language processing model may include neural networks or neural net algorithms, including convolutional neural networks or convolutional neural net processes [0026] - suggest, as a function of analyzing, a modification to a target text 152 of user inputted legal text 136, and generate a score 156 for a modified user legal document 168);
and presenting the suggested modification to a user in the user interface ([0026] - and outputting a suggested modification 160 to user inputted legal text 136 to user device 140).
Regarding claim 2, Guberman discloses the computer-implemented method, wherein presenting the suggested modification to the user in the user interface is based at least in part on a confidence score of the suggested modification ([0043] - Computing device 104 may provide the certain word, such as by autofill and/or suggested modification 160, to user when the computing device 104 identifies the two or more consecutive words if the probability is high. In some embodiments, computing device 104 may provide the certain word if computing device 104 determines that the probability of two or more consecutive words in user inputted legal text 136 and/or target text 152 will be followed by a certain word exceeds a threshold probability).
Regarding claim 3, Guberman discloses the computer-implemented method, wherein presenting the suggested modification to the user in the user interface comprises:
determining that the confidence score of the suggested modification exceeds a threshold ([0043] - Computing device 104 may provide the certain word, such as by autofill and/or suggested modification 160, to user when the computing device 104 identifies the two or more consecutive words if the probability is high. In some embodiments, computing device 104 may provide the certain word if computing device 104 determines that the probability of two or more consecutive words in user inputted legal text 136 and/or target text 152 will be followed by a certain word exceeds a threshold probability);
and in response, presenting the suggested modification ([0043] - Computing device 104 may provide the certain word, such as by autofill and/or suggested modification 160, to user when the computing device 104 identifies the two or more consecutive words if the probability is high. In some embodiments, computing device 104 may provide the certain word if computing device 104 determines that the probability of two or more consecutive words in user inputted legal text 136 and/or target text 152 will be followed by a certain word exceeds a threshold probability).
Regarding claim 4, Guberman discloses the computer-implemented method, wherein presenting the suggested modification to the user in the user interface further comprises: automatically inserting the suggested modification in the body of text, wherein the suggested modification is in bold form ([0057] - Target text 152 or text to which suggested modification 160 is directed to may be brought to user's attention by altering its appearance, for example and without limitation, by highlighting, bolding and/or underlining, or the like, among others).
Regarding claim 5, Guberman discloses the computer-implemented method, wherein presenting the suggested modification to the user in the user interface further comprises: presenting the suggested modification in a color different than a color of the body of text, wherein the user can accept the suggested modification ([0057] - Target text 152 or text to which suggested modification 160 is directed to may be brought to user's attention by altering its appearance, for example and without limitation, by highlighting, bolding and/or underlining, or the like, among others).
Regarding claim 6, Guberman discloses the computer-implemented method, wherein presenting the suggested modification to the user in the user interface further comprises: indicating the suggested modification to the user as an icon that the user can inspect and determine whether to accept the suggested modification ([0058] - User 144 may have the ability to completely ignore, completely accept or partially accept suggested modification 160, as desired).
Regarding claim 7, Guberman discloses the computer implemented method, wherein the body of text comprises text from a source document corresponding to the text segment, text from a clipboard of the user interface, or both ([0025] - language processing module 108 may use a corpus of documents (e.g. legal source texts from legal sources) to generate associations between language elements in a language processing module).
Regarding claim 8, Guberman discloses the computer implemented method, wherein the trained neural network is a language model ([0020] - natural language processing model may include neural networks or neural net algorithms, including convolutional neural networks or convolutional neural net processes [0026] - suggest, as a function of analyzing, a modification to a target text 152 of user inputted legal text 136, and generate a score 156 for a modified user legal document 168).
Regarding claim 9, Guberman discloses the computer implemented method, wherein the suggested modification is in a domain-specific language ([0041] - language processing module 108 may use domain-specific text corpora).
Regarding claim 10, Guberman discloses the computer-implemented method, wherein the trained neural network has been trained on a plurality of training examples, each training example corresponding to an insertion event and comprising:
an original text segment that has been inserted into an original body of text ([0050] - Training data may include individual words and/or phrases and correlating sentiment. Training data may be manually inputted);
an original context comprising text surrounding the original text segment in the original body of text ([0047] - The use of higher-order n-grams may allow for the higher-order natural language processing model to capture more of the context around a given word or letter);
and data identifying any edits that were made to the original text segment or the original context after the original text segment was inserted into the original body of text ([0050] - Training data may include individual words and/or phrases and correlating sentiment. Training data may be manually inputted, for example. For example, training data may include the word “worst” and correlating negative sentiment. Machine-learning model may be configured to output sentiment based on training data and inputted text, such as user inputted legal text 136 and/or user legal document 148. Computing device 104 may be configured to determine an average sentiment for a sentence, paragraph, and/or section of user inputted legal text 136 and/or user legal document 148 based on output from machine-learning model and generate heat map based on the determination).
Regarding claim 11, Guberman discloses a system comprising one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform the operations comprising:
receiving information comprising a text segment to be inserted into a body of text displayed in a user interface ([0025] - experts may identify or enter such documents via graphical user interface [0026] - receive user inputted legal text 136 from a user device 140 being operated by a human user 144 to create a user legal document 148, analyze user inputted legal text 136 using natural language processing model 112);
identifying a context, wherein the context is based at least in part on text surrounding the inserted text segment in the body of text ([0072] - Speech tagging or part-of-speech (POS) tagging, sometimes also called grammatical tagging, may be considered to be a process of marking up a word in a text (corpus) as corresponding to a particular part of speech, based on both its definition and its context);
generating an input that comprises the text segment and the context ([0072] - Speech tagging or part-of-speech (POS) tagging, sometimes also called grammatical tagging, may be considered to be a process of marking up a word in a text (corpus) as corresponding to a particular part of speech, based on both its definition and its context);
processing the input using a trained neural network to generate a suggested modification to the inserted text segment, to the context, or both ([0020] - natural language processing model may include neural networks or neural net algorithms, including convolutional neural networks or convolutional neural net processes [0026] - suggest, as a function of analyzing, a modification to a target text 152 of user inputted legal text 136, and generate a score 156 for a modified user legal document 168);
and presenting the suggested modification to a user in the user interface ([0026] - and outputting a suggested modification 160 to user inputted legal text 136 to user device 140).
Dependent claims 12-15 are analogous in scope to claims 2-5, and are rejected according to the same reasoning.
Regarding claim 16, Guberman discloses one or more computer storage media storing instructions that when executed by one or more computers cause the one or more computers to perform operations comprising:
receiving information comprising a text segment to be inserted into a body of text displayed in a user interface ([0025] - experts may identify or enter such documents via graphical user interface [0026] - receive user inputted legal text 136 from a user device 140 being operated by a human user 144 to create a user legal document 148, analyze user inputted legal text 136 using natural language processing model 112);
identifying a context, wherein the context is based at least in part on text surrounding the inserted text segment in the body of text ([0072] - Speech tagging or part-of-speech (POS) tagging, sometimes also called grammatical tagging, may be considered to be a process of marking up a word in a text (corpus) as corresponding to a particular part of speech, based on both its definition and its context);
generating an input that comprises the text segment and the context ([0072] - Speech tagging or part-of-speech (POS) tagging, sometimes also called grammatical tagging, may be considered to be a process of marking up a word in a text (corpus) as corresponding to a particular part of speech, based on both its definition and its context);
processing the input using a trained neural network to generate a suggested modification to the inserted text segment, to the context, or both ([0020] - natural language processing model may include neural networks or neural net algorithms, including convolutional neural networks or convolutional neural net processes [0026] - suggest, as a function of analyzing, a modification to a target text 152 of user inputted legal text 136, and generate a score 156 for a modified user legal document 168);
and presenting the suggested modification to a user in the user interface ([0026] - and outputting a suggested modification 160 to user inputted legal text 136 to user device 140).
Dependent claims 17-20 are analogous in scope to claims 2-5, and are rejected according to the same reasoning.
Conclusion
7. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Alikaniotis (U.S. Publication No. 20230289529) teaches detecting the tone of text. Sampaio de Rezende (U.S. Publication No. 20230073843) teaches data compatibility for text-enhanced visual retrieval.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ETHAN DANIEL KIM whose telephone number is (571) 272-1405. The examiner can normally be reached on Monday - Friday 9:00 - 5:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Richemond Dorvil can be reached on (571) 272-7602. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ETHAN DANIEL KIM/
Examiner, Art Unit 2658
/RICHEMOND DORVIL/Supervisory Patent Examiner, Art Unit 2658