DETAILED ACTION
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 .
Status of Claims
This office action is in response to the patent application 18/792,382 originally filed on August 1, 2024. Claims 1-12 were originally presented for examination. Claim 1 is independent.
In the preliminary amendment filed November 21, 2024, claims 8 and 12 were amended. Claims 1-12 remain pending.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55 on November 26, 2024. This application claims foreign priority of CN202310978221.5 (People’s Republic of China), filed August 4, 2023.
Claim Objections
Claims 3-7 and 9 are objected to because of the following informalities: claims containing more than one period.
Claim 3, and substantially similar limitations in claims 4-7 and 9, is objected to because steps “a,” “b,” “c,” “d,” “e,” “f,” “g,” “h,” “i,” “j,” “k,” “l,” “m,” “n,” “o,” “p,” “q,” “r,” “s,” “t,” “u,” and “v” each have a period after them. These periods should be removed, or substituted with a “)”, as each claim can only have one period. Per MPEP 608.01(m), each claim begins with a capital letter and ends with a period. Periods may not be used elsewhere in the claims except for abbreviations. See Fressola v. Manbeck, 36 USPQ2d 1211 (D.D.C. 1995). Appropriate correction is required.
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.
Claims 1-12 are rejected under 35 U.S.C. § 112(b), as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claim 1 recites the limitation “the cleaned and preprocessed materials.” The limitation is not previously introduced in claim 1. As such, the limitation lacks antecedent basis. Therefore, claim 1 is rejected under 35 U.S.C. § 112(b), as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention. Claims 2-12 are also rejected under 35 U.S.C. § 112(b), based on their respective dependencies to claim 1.
Claim 1, and substantially similar limitations in claim 11, recites the limitation “users.” The limitation is originally introduced in claim 1. As such, the subsequent limitations are either (1) not following antecedent basis (i.e. “the users”); or (2) are intended to be new limitations which ambiguously conflict with the previous limitation of claim 1. Therefore, claims 1 and 11 are rejected under 35 U.S.C. § 112(b), as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention. Claims 2-12 are also rejected under 35 U.S.C. § 112(b), based on their respective dependencies to claim 1.
Claim 1 recites the limitation “the preprocessed natural language information.” The limitation is not previously introduced in claim 1. As such, the limitation lacks antecedent basis. Therefore, claim 1 is rejected under 35 U.S.C. § 112(b), as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention. Claims 2-12 are also rejected under 35 U.S.C. § 112(b), based on their respective dependencies to claim 1.
Claim 1 recites the limitation “the understood content.” The limitation is not previously introduced in claim 1. As such, the limitation lacks antecedent basis. Therefore, claim 1 is rejected under 35 U.S.C. § 112(b), as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention. Claims 2-12 are also rejected under 35 U.S.C. § 112(b), based on their respective dependencies to claim 1.
Claim 1, and substantially similar limitations in claim 5, recites the limitation “the best-matched learning materials.” The limitation is not previously introduced in claim 1 or 5. As such, the limitation lacks antecedent basis. Therefore, claims 1 and 5 are rejected under 35 U.S.C. § 112(b), as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention. Claims 2-12 are also rejected under 35 U.S.C. § 112(b), based on their respective dependencies to claim 1.
Claim 1, and a substantially similar limitation in claim 5, recites the limitation “the knowledge points.” The limitation “related knowledge points” is originally introduced in claim 1. As such, the subsequent limitations are either (1) not following antecedent basis (i.e. “the related knowledge points”); or (2) are intended to be new limitations which ambiguously conflict with the previous limitation of claim 1. Therefore, claims 1 and 5 are rejected under 35 U.S.C. § 112(b), as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention. Claims 2-12 are also rejected under 35 U.S.C. § 112(b), based on their respective dependencies to claim 1.
Claim 1 recites the limitation “the question.” The limitation is not previously introduced in claim 1. As such, the limitation lacks antecedent basis. Therefore, claim 1 is rejected under 35 U.S.C. § 112(b), as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention. Claims 2-12 are also rejected under 35 U.S.C. § 112(b), based on their respective dependencies to claim 1.
Claim 1 recites the limitation “the response.” The limitation is not previously introduced in claim 1. As such, the limitation lacks antecedent basis. Therefore, claim 1 is rejected under 35 U.S.C. § 112(b), as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention. Claims 2-12 are also rejected under 35 U.S.C. § 112(b), based on their respective dependencies to claim 1.
Claim 1 recites the limitation “the evaluation.” The limitation is not previously introduced in claim 1. As such, the limitation lacks antecedent basis. Therefore, claim 1 is rejected under 35 U.S.C. § 112(b), as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention. Claims 2-12 are also rejected under 35 U.S.C. § 112(b), based on their respective dependencies to claim 1.
Claim 1 recites the limitation “the corresponding requirements.” The limitation is not previously introduced in claim 1. As such, the limitation lacks antecedent basis. Therefore, claim 1 is rejected under 35 U.S.C. § 112(b), as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention. Claims 2-12 are also rejected under 35 U.S.C. § 112(b), based on their respective dependencies to claim 1.
Claim 7 recites the limitation “the requirements.” The limitation is not previously introduced in claim 1 or 7. As such, the limitation lacks antecedent basis. Therefore, claim 7 is rejected under 35 U.S.C. § 112(b), as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claim 5, and substantially similar limitations in claim 6, recites the limitation “the key points.” The limitation is not previously introduced in claim 1, 5, or 6. As such, the limitation lacks antecedent basis. Therefore, claims 5 and 6 are rejected under 35 U.S.C. § 112(b), as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
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 1-12 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim 1 is directed to “a deep learning-based natural language understanding method” (i.e. a process) and claim 8 is directed to “an AI teaching assistant system” (i.e. a machine), hence the claims are directed to one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter). In other words, Step 1 of the subject-matter eligibility analysis is “Yes.”
However, the claims are drawn to an abstract idea of “natural language understanding,” either in the form of “certain methods of organizing human activity,” in terms of managing personal behavior or relationships or interactions between people (including social activities, teaching and following rules or instructions), or reasonably in the form of “mental processes,” in terms of processes that can be performed in the human mind (including an observation, evaluation, judgement or opinion). Claims that require a computer may also recite a mental process, as described in MPEP 2106.04(a)(2)(III)(C).
Regardless, the claims are reasonably understood as either “certain methods of organizing human activity” or “mental processes,” which require the following limitations:
“S1: constructing a knowledge database by first obtaining various forms of learning materials that are pre-stored or uploaded by users, cleaning and preprocessing these learning materials, and then organizing the cleaned and preprocessed materials into documents and saving them into the knowledge database;
S2: constructing a question database by preprocessing various forms of natural language information input by users, and then saving the preprocessed natural language information into the question database;
S3: learning and understanding the natural language information in the question database, searching for related knowledge points in the knowledge database based on the understood content, and selecting the best-matched learning materials corresponding to the knowledge points as samples to respond to the natural language information using one or more scoring or matching algorithms;
S4: generating a record including the question, the response, and the evaluation, and saving it into the knowledge database; and
S5: generating multiple forms of responses and outputting them according to the corresponding requirements.”
These limitations simply describe a process of data gathering and manipulation, which is partially analogous to “collecting information, analyzing it, and displaying certain results of the collection analysis” (i.e. Electric Power Group, LLC, v. Alstom, 830 F.3d 1350, 119 U.S.P.Q.2d 1739 (Fed. Cir. 2016)). Hence, these limitations are akin to an abstract idea which has been identified among non-limiting examples to be an abstract idea. In other words, Step 2A, Prong 1 of the subject-matter eligibility analysis is “Yes.”
Furthermore, the claims do not include additional elements that either alone or in combination are sufficient to claim a practical application because to the extent that, e.g., “an AI teaching assistant system,” “a cloud backend,” “a user terminal,” “a hardware carrier,” and “a computer storage medium” are claimed, as these are merely claimed to add insignificant extra-solution activity to the judicial exception (e.g., data gathering) and/or do no more than generally link the use of a judicial exception to a particular technological environment or field of use. In other words, the claimed “natural language understanding” is not providing a practical application, thus Step 2A, Prong 2 of the subject-matter eligibility analysis is “No.”
Likewise, the claims do not include additional elements that either alone or in combination are sufficient to amount to significantly more than the judicial exception because to the extent that, e.g. “an AI teaching assistant system,” “a cloud backend,” “a user terminal,” “a hardware carrier,” and “a computer storage medium” are claimed these are all generic, well-known, and conventional computing elements. As evidence that these are generic, well-known, and conventional computing elements, Applicant’s specification discloses them in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a), per MPEP § 2106.07(a) III (a), which satisfies the Examiner’s evidentiary burden requirement per the Berkheimer memo.
None of the identified additional elements of the claims, “an AI teaching assistant system,” “a cloud backend,” “a user terminal,” “a hardware carrier,” and “a computer storage medium,” are explicitly defined in the specification. Therefore, these elements must be interpreted using the plain meaning of the terminology in the art by one having ordinary skill in the art.
Each of the identified additional elements is reasonably interpreted as a generic computer or generic computer component, which provides no details of anything beyond ubiquitous standard equipment. As such, the claimed limitations of “an AI teaching assistant system,” “a cloud backend,” “a user terminal,” “a hardware carrier,” and “a computer storage medium” are reasonably understood as not providing anything significantly more than the judicial exception. Therefore, Step 2B, of the subject-matter eligibility analysis is “No.”
In addition, dependent claims 2-12 do not provide a practical application and are insufficient to amount to significantly more than the judicial exception. As such, dependent claims 2-12 are also rejected under 35 U.S.C. § 101, based on their respective dependencies to independent claim 1.
Therefore, claims 1-12 are rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 102
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.
Claims 1, 4, 5, and 12 are rejected under 35 U.S.C. 102(a)(1) and 35 U.S.C. 102(a)(2) as being anticipated by Chen et al. (hereinafter “Chen,” US 2014/0358890).
Regarding claim 1, Chen discloses a deep learning-based natural language understanding method, comprising the following steps:
S1: constructing a knowledge database by first obtaining various forms of learning materials that are pre-stored or uploaded by users, cleaning and preprocessing these learning materials, and then organizing the cleaned and preprocessed materials into documents and saving them into the knowledge database (Chen [0039-0040], “The knowledge databases may be structured, semi-structured or unstructured. A first exemplary type of knowledge database provides a structured collection of formulated or prepared question-answer pairs. Such question-answer paired knowledge databases include, but are not limited to, Yahoo! Answers, WikiAnswer and Baidu Zhidao… A second exemplary type of knowledge database provides a semi-structured or unstructured collection of natural language documents containing plain text paragraphs. Such plain text knowledge databases may include, but are not limited to, public or private databases or knowledge bases, Intranets, the Internet, web-pages (e.g., news website, domain-based website, Wikipedia, etc.), which can be searched and/or crawled for content. In some implementations, the data collector 202 retrieves the plain text from the plain text knowledge databases and preprocesses the plain text before storing it in the database 129. The plain text may be preprocessed by categorizing it,” wherein if the database is structured or semi-structured, some level of cleaning and preprocessing is required before storing data into it);
S2: constructing a question database by preprocessing various forms of natural language information input by users, and then saving the preprocessed natural language information into the question database (Chen [0039], “first exemplary type of knowledge database provides a structured collection of formulated or prepared question-answer pairs. Such question-answer paired knowledge databases include, but are not limited to, Yahoo! Answers, WikiAnswer and Baidu Zhidao,” a type of knowledge database that stores question-answer pairs);
S3: learning and understanding the natural language information in the question database, searching for related knowledge points in the knowledge database based on the understood content, and selecting the best-matched learning materials corresponding to the knowledge points as samples to respond to the natural language information using one or more scoring or matching algorithms (Chen [0053], “the question parser 204 includes or invokes functions from a natural language processing (NLP) library for parsing the natural-language input question. NLP may also be used to parse document content from the knowledge databases (205a-205n) and extract more detailed semantic and linguistic information.”; also Chen [0088], “Before computing the score and ranking such candidate paragraph, the answer retrieval and ranking unit 206 may segment the long text of the candidate paragraph into smaller segments of text.”; also Chen [0122], “all the candidate answers are ranked to find the final best one. The ranking may be based on one or more pre-defined heuristic rules. For example, if the candidate answer was retrieved from a question-answer paired KB and its SCORE ( ) is greater than or equal to 80%, the candidate answer will be returned as the final answer.”);
S4: generating a record including the question, the response, and the evaluation, and saving it into the knowledge database (Chen [0121-0122], “Each type of knowledge database (e.g., question-answer paired KB, plain text KB, RDF KB, etc.) may yield zero or one candidate answer. Where there are multiple types of knowledge databases, there may be multiple candidate answers… all the candidate answers are ranked to find the final best one. The ranking may be based on one or more pre-defined heuristic rules. For example, if the candidate answer was retrieved from a question-answer paired KB and its SCORE ( ) is greater than or equal to 80%, the candidate answer will be returned as the final answer.”); and
S5: generating multiple forms of responses and outputting them according to the corresponding requirements (Chen [0121], “the QA processor 203 evaluates the candidate answers to find the final answer to be returned to the user. Each type of knowledge database (e.g., question-answer paired KB, plain text KB, RDF KB, etc.) may yield zero or one candidate answer. Where there are multiple types of knowledge databases, there may be multiple candidate answers.”).
Regarding claim 4, Chen discloses wherein the learning and understanding of natural language information in the question database in step S3 includes comprises: f. extracting key points: using Al large language models to learn and extract several key points from the natural language information; g. understanding key points: using natural language processing models to understand and record each key point (Chen [0062], “Yet another property that may be identified based on the input question is the "focus". "Focus" generally refers to a sequence of words that defines what the input question is looking for. For example, in the question "What is the capital of China?", the question parser 204 understands that the question is asking about a LOCATION. However, the question class LOCATION may be too broad, and it may be helpful to narrow down the search to focus on CAPITAL. This means that the answer should be the name of a capital (or city name). The question parser 204 may identify the focus by extracting, from the input question, the first noun after the question class word (e.g., CAPITAL).”).
Regarding claim 5, Chen discloses wherein the selection of the best-matched learning materials corresponding to the knowledge points to respond to natural language information in step S3 comprises: h. searching for related learning materials: comparing the key points in the natural language information with each knowledge point in the knowledge database, and finding several knowledge points that are closest to the key points in the vector space; i. selecting the best-matched learning materials: comparing the selected learning materials with the key points in the natural language information and choosing the best-matched learning materials; j. responding using learning materials: using the selected best-matched learning materials combined with the trained Al large language model to respond to the natural language information (Chen [0062], “Yet another property that may be identified based on the input question is the "focus". "Focus" generally refers to a sequence of words that defines what the input question is looking for. For example, in the question "What is the capital of China?", the question parser 204 understands that the question is asking about a LOCATION. However, the question class LOCATION may be too broad, and it may be helpful to narrow down the search to focus on CAPITAL. This means that the answer should be the name of a capital (or city name). The question parser 204 may identify the focus by extracting, from the input question, the first noun after the question class word (e.g., CAPITAL),” searching the key points; also Chen [0122], “Since it is not easy to compare the candidate answers obtained from the different types of knowledge databases, the QA processor 203 may use one or more pre-defined heuristic rules based on the question type to determine the final answer. In some implementations, all the candidate answers are ranked to find the final best one. The ranking may be based on one or more pre-defined heuristic rules. For example, if the candidate answer was retrieved from a question-answer paired KB and its SCORE ( ) is greater than or equal to 80%, the candidate answer will be returned as the final answer. Such candidate answer is deemed the most accurate and thus assigned the highest priority”).
Regarding claim 12, Chen discloses a computer storage medium, comprising a computer storage medium which stores several computer instructions, wherein the computer instructions, when invoked, execute all or part of the steps of the deep learning-based natural language understanding method according to claim 1 (see Chen claim 19, “A non-transitory computer-readable medium having stored thereon program code, the program code executable by a computer”).
Claim Rejections - 35 USC § 103
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Chen in view of Dong et al. (hereinafter “Dong,” US 2019/0087472).
Regarding claim 6, Chen does not explicitly teach every limitation of wherein step S4 further includes a self-learning scoring process, which comprises: k. collecting and recording user feedback on responses, including positive and negative feedback; I. scoring the responses based on the collected feedback using certain scoring rules; m. using the scoring results for response optimization, which comprises adjusting parameter weights, re-understanding the key points of instruction questions, and regenerating more detailed and accurate responses.
However, Dong discloses wherein step S4 further includes a self-learning scoring process, which comprises: k. collecting and recording user feedback on responses, including positive and negative feedback; I. scoring the responses based on the collected feedback using certain scoring rules; m. using the scoring results for response optimization, which comprises adjusting parameter weights, re-understanding the key points of instruction questions, and regenerating more detailed and accurate responses (Dong [0056], “the user may continue to provide feedback on the new service result as the user receives a new service result. The intelligent service system continuously adjusts the search term and/or the weight by continuously collecting user feedback, and constitutes a closed-loop process of training, learning and continuous optimization, which may continuously improve the quality of the returned service result, to provide to the user the best answer through training and learning. Through this interaction, the intelligent service system greatly improves the probability of providing a satisfactory service result in a short period, thereby greatly improving the user experience.”).
Dong is analogous to Chen, as both are drawn to the art of artificial intelligence systems. It would be obvious to try by one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method as taught by Chen, to include wherein step S4 further includes a self-learning scoring process, which comprises: k. collecting and recording user feedback on responses, including positive and negative feedback; I. scoring the responses based on the collected feedback using certain scoring rules; m. using the scoring results for response optimization, which comprises adjusting parameter weights, re-understanding the key points of instruction questions, and regenerating more detailed and accurate responses, as taught by Dong, since it combines prior art elements according to known methods of training machine learning models to yield predictable results. Doing so is a predictable solution that one of ordinary skill in the art could have pursued with a reasonable expectation of success.
Claim 8, 10, and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Chen in view of Zadeh et al. (hereinafter “Zadeh,” US 2022/0121884).
Regarding claim 8, Chen does not explicitly teach every limitation of an Al teaching assistant system, comprising a cloud backend and a user terminal, wherein the user terminal collects various instruction questions input by the user and the evaluation information on the responses, and transmits them to the cloud backend; the cloud backend processes the instruction questions using the deep learning-based natural language understanding method according to claim 1, and feeds back the response information to the user terminal; and the user chooses whether to evaluate the responses and provide evaluation content via the terminal based on the received response information.
However, Zadeh discloses an Al teaching assistant system, comprising a cloud backend and a user terminal, wherein the user terminal collects various instruction questions input by the user and the evaluation information on the responses, and transmits them to the cloud backend; the cloud backend processes the instruction questions using the deep learning-based natural language understanding method according to claim 1, and feeds back the response information to the user terminal; and the user chooses whether to evaluate the responses and provide evaluation content via the terminal based on the received response information (Zadeh [2698], “data is processed by multiple processors in parallel (labeled 1 through 5), e.g., in a computing cloud (e.g., with a distributed file system), and the results of processing are aggregated in a aggregation node (A).”; also Zadeh [2700], “The system can be on a computer, PDA, tablet computer, phone, smart phone, electronic device, game device, e-book reader, game console, communication device, computing device, PC, server, terminal, kiosk, video game, entertainment device, music box, music player, multimedia device, movie player, calendar device, watch, clock, or the like.”).
Zadeh is analogous to Chen, as both are drawn to the art of artificial intelligence systems. It would be obvious to try by one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method as taught by Chen, to include an Al teaching assistant system, comprising a cloud backend and a user terminal, wherein the user terminal collects various instruction questions input by the user and the evaluation information on the responses, and transmits them to the cloud backend; the cloud backend processes the instruction questions using the deep learning-based natural language understanding method according to claim 1, and feeds back the response information to the user terminal; and the user chooses whether to evaluate the responses and provide evaluation content via the terminal based on the received response information, as taught by Zadeh, since it combines prior art elements according to known methods of training machine learning models to yield predictable results. Doing so is a predictable solution that one of ordinary skill in the art could have pursued with a reasonable expectation of success.
Regarding claim 10, Chen in view of Zadeh discloses wherein the user terminal is a hardware carrier with a user interaction interface and multiple forms of response output modules (see Chen claim 19, “A non-transitory computer-readable medium having stored thereon program code, the program code executable by a computer”).
Regarding claim 11, Chen in view of Zadeh discloses wherein the user terminal supports users in uploading learning materials in various forms comprising text, voice, video, and image, which are stored into the knowledge database by the cloud backend through the knowledge base storage module (see Chen claim 19, “A non-transitory computer-readable medium having stored thereon program code, the program code executable by a computer”).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Stephen Alvesteffer whose telephone number is (571)272-8680. The examiner can normally be reached M-F 8:00-6:00.
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, Peter Vasat can be reached at 571-270-7625. 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.
/STEPHEN ALVESTEFFER/Examiner, Art Unit 3715
Prosecution Insights