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
The information disclosure statement(s) (IDS) submitted on 30 June 2023 is/are being considered by the examiner.
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.
Claim [1-18] rejected under 35 U.S.C. 101 because the claimed invention is directed to a mental process without significantly more.
Regarding claim 1, a computer-implemented method (CIM) comprising: receiving a first historical reading data set for a first user;
determining a plurality of reader attribute values of a user reading profile for the first user, with the attribute values of the user reading profile for the first user including a first reading level attribute value that indicates a level of knowledge of the first user with respect to a first topic based on the first historical reading data set; receiving a request for a natural language summary, for the first user, of an original text written in natural language text and relating to the first topic; responsive to the receipt of the request, preparing a first summary of the original text in accordance with the attribute values of the user reading profile for the first user; and presenting the first summary on a computer device of the first user.
[A person could review a user’s reading history, estimate the user’s topic knowledge, receive a request, rewrite a text at the appropriate level, and give the summary to the user with the aid of a pen and paper].
As described above, these limitations can be carried out as a series of mental steps.
This judicial exception is not integrated into a practical application because the only additional element recited is a computer-implemented method and a computer device, and these additional elements are nothing more than using a generic computer as a tool to implement the mental process.
This claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as described above, the only additional elements recited are a computer implemented method and a computer device, and these additional elements are nothing more than instructions to apply the mental process using general-purpose hardware.
Regarding claim 2, the CIM of claim 1 further comprising: subsequent to the presentation of the first summary, receiving a second historical reading data set for a first user; performing incremental concept progression on the second historical reading data set to refine the attribute values of the user reading profile.
[A person could review later reading activity and update the user’s profile as the user progresses.]
As described above, these limitations can be carried out as a series of mental steps.
No additional elements are recited. Therefore, the claim does not describe a practical application or significantly more than the mental process.
Regarding claim 3, recite the CIM of claim 2 wherein the performance of concept progression refines at least the first reading level attribute.
[A person could use later reading activity to revise an assessment of the user’s knowledge of a particular topic.]
As described above, these limitations can be carried out as a series of mental steps.
No additional elements are recited. Therefore, the claim does not describe a practical application or significantly more than the mental process.
Regarding claim 4, recite the CIM of claim 2 wherein the performance of concept progression refines at least a second reading level attribute reflecting general reading comprehension abilities of the first user.
[A person could use later reading activity to revise an assessment of the user’s general reading comprehension ability].
As described above, these limitations can be carried out as a series of mental steps.
No additional elements are recited. Therefore, the claim does not describe a practical application or significantly more than the mental process.
Regarding claim 5, the CIM of claim 2 wherein the performance of concept progression refines at least a second reading level attribute reflecting vocabulary level of the first user.
[A person could use later reading activity to revise an assessment of the user’s vocabulary level.]
As described above, these limitations can be carried out as a series of mental steps.
No additional elements are recited. Therefore, the claim does not describe a practical application or significantly more than the mental process.
Regarding claim 6, recite the CIM of claim 2 wherein the performance of concept progression uses Natural Language Processing relative to a multiplicity of topics by capturing user inputs and behavior of the first user.
[A person could evaluate the user’s words, responses, and behavior across multiple topics to update the user’s progression. Merely requiring a generic “Natural language processing does not provide a specific technological improvement].
As described above, these limitations can be carried out as a series of mental steps.
This judicial exception is not integrated into a practical application because the only additional element recited is using Natural Language processing and this additional element is nothing more than generic computer implementing the mental process using general-purpose software and general-purpose hardware.
This claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as described above, the only additional element recited is using Natural Language processing and this additional element is nothing more than instructions to apply the mental process using general-purpose software and hardware.
Regarding claim 7, recites a computer program product (CPP) comprising: a set of storage device(s); and computer code stored collectively in the set of storage device(s), with the computer code including data and instructions to cause a processor(s) set to perform at least the following operations: receiving a first historical reading data set for a first user, determining a plurality of reader attribute values of a user reading profile for the first user, with the attribute values of the user reading profile for the first user including a first reading level attribute value that indicates a level of knowledge of the first user with respect to a first topic based on the first historical reading data set, receiving a request for a natural language summary, for the first user, of an original text written in natural language text and relating to the first topic, responsive to the receipt of the request, preparing a first summary of the original text in accordance with the attribute values of the user reading profile for the first user, and presenting the first summary on a computer device of the first user.
[A person could review a user’s reading history, estimate the user’s topic knowledge, receive a request, rewrite a text at the appropriate level, and give the summary to the user with the aid of a pen and paper].
As described above, these limitations can be carried out as a series of mental steps.
This judicial exception is not integrated into a practical application because the only additional elements recited are a computer program product, a set of storage devices, computer code stored collectively in the set of storage device, and a computer device, and these additional elements are nothing more than generic computer implementing the mental process using general-purpose software and general-purpose hardware.
This claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as described above, the only additional element recited are a computer program product, a set of storage devices, computer code stored collectively in the set of storage device, and a computer device, and these additional elements are nothing more than instructions to apply the mental process using general-purpose software and hardware.
Regarding claim 8, recites the CPP of claim 7 wherein the computer code further includes instructions for causing the processor(s) set to perform the following operation(s): subsequent to the presentation of the first summary, receiving a second historical reading data set for a first user; performing incremental concept progression on the second historical reading data set to refine the attribute values of the user reading profile.
[A person could review later reading activity and update the user’s profile as the user progresses.]
As described above, these limitations can be carried out as a series of mental steps.
No additional elements are recited. Therefore, the claim does not describe a practical application or significantly more than the mental process.
Regarding claim 9, recites the CPP of claim 8 wherein the performance of concept progression refines at least the first reading level attribute.
[A person could use later reading activity to revise an assessment of the user’s knowledge of a particular topic.]
As described above, these limitations can be carried out as a series of mental steps.
No additional elements are recited. Therefore, the claim does not describe a practical application or significantly more than the mental process.
Regarding claim 10, recites the CPP of claim 8 wherein the performance of concept progression refines at least a second reading level attribute reflecting general reading comprehension abilities of the first user.
[A person could use later reading activity to revise an assessment of the user’s general reading comprehension ability].
As described above, these limitations can be carried out as a series of mental steps.
No additional elements are recited. Therefore, the claim does not describe a practical application or significantly more than the mental process.
Regarding claim 11, recites the CPP of claim 8 wherein the performance of concept progression refines at least a second reading level attribute reflecting vocabulary level of the first user.
[A person could use later reading activity to revise an assessment of the user’s vocabulary level.]
As described above, these limitations can be carried out as a series of mental steps.
No additional elements are recited. Therefore, the claim does not describe a practical application or significantly more than the mental process.
Regarding claim 12, recites the CPP of claim 8 wherein the performance of concept progression uses Natural Language Processing relative to a multiplicity of topics by capturing user inputs and behavior of the first user.
[A person could evaluate the user’s words, responses, and behavior across multiple topics to update the user’s progression. Merely requiring a generic “Natural language processing does not provide a specific technological improvement].
As described above, these limitations can be carried out as a series of mental steps.
This judicial exception is not integrated into a practical application because the only additional element recited is using Natural Language processing and this additional element is nothing more than generic computer implementing the mental process using general-purpose software and general-purpose hardware.
This claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as described above, the only additional element recited is using Natural Language processing and this additional element is nothing more than instructions to apply the mental process using general-purpose software and hardware.
.
Regarding claim 13, recites a computer system (CS) comprising: a processor(s) set; a set of storage device(s); and computer code stored collectively in the set of storage device(s), with the computer code including data and instructions to cause the processor(s) set to perform at least the following operations: receiving a first historical reading data set for a first user, determining a plurality of reader attribute values of a user reading profile for the first user, with the attribute values of the user reading profile for the first user including a first reading level attribute value that indicates a level of knowledge of the first user with respect to a first topic based on the first historical reading data set, receiving a request for a natural language summary, for the first user, of an original text written in natural language text and relating to the first topic, responsive to the receipt of the request, preparing a first summary of the original text in accordance with the attribute values of the user reading profile for the first user, and presenting the first summary on a computer device of the first user.
[A person could review a user’s reading history, estimate the user’s topic knowledge, receive a request, rewrite a text at the appropriate level, and give the summary to the user with the aid of a pen and paper].
As described above, these limitations can be carried out as a series of mental steps.
This judicial exception is not integrated into a practical application because the additional elements recited are processor, storage devices, computer code and a computer device and all these additional elements are nothing more than generic computer implementing the mental process using general-purpose software and general-purpose hardware.
This claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as described above, the additional elements recited are processor, storage devices, computer code and a computer device and these additional elements are nothing more than instructions to apply the mental process using general-purpose software and hardware.
Regarding claim 14, recites the CS of claim 13 wherein the computer code further includes instructions for causing the processor(s) set to perform the following operation(s): subsequent to the presentation of the first summary, receiving a second historical reading data set for a first user; performing incremental concept progression on the second historical reading data set to refine the attribute values of the user reading profile.
[A person could review later reading activity and update the user’s profile as the user progresses.]
As described above, these limitations can be carried out as a series of mental steps.
No additional elements are recited. Therefore, the claim does not describe a practical application or significantly more than the mental process.
Regarding claim 15, recites the CS of claim 14 wherein the performance of concept progression refines at least the first reading level attribute.
[A person could use later reading activity to revise an assessment of the user’s knowledge of a particular topic.]
As described above, these limitations can be carried out as a series of mental steps.
No additional elements are recited. Therefore, the claim does not describe a practical application or significantly more than the mental process.
Regarding claim 16, recites the CIM of claim 14 wherein the performance of concept progression refines at least a second reading level attribute reflecting general reading comprehension abilities of the first user.
[A person could use later reading activity to revise an assessment of the user’s general reading comprehension ability].
As described above, these limitations can be carried out as a series of mental steps.
No additional elements are recited. Therefore, the claim does not describe a practical application or significantly more than the mental process.
Regarding claim 17, recites the CIM of claim 14 wherein the performance of concept progression refines at least a second reading level attribute reflecting vocabulary level of the first user.
[A person could use later reading activity to revise an assessment of the user’s vocabulary level.]
As described above, these limitations can be carried out as a series of mental steps.
No additional elements are recited. Therefore, the claim does not describe a practical application or significantly more than the mental process.
Regarding claim 18 recites the CIM of claim 14 wherein the performance of concept progression uses Natural Language Processing relative to a multiplicity of topics by capturing user inputs and behavior of the first user.
[A person could evaluate the user’s words, responses, and behavior across multiple topics to update the user’s progression. Merely requiring a generic “Natural language processing does not provide a specific technological improvement].
As described above, these limitations can be carried out as a series of mental steps.
This judicial exception is not integrated into a practical application because the only additional element recited is using Natural Language processing and this additional element is nothing more than generic computer implementing the mental process using a general-purpose software model and general-purpose hardware.
This claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as described above, the only additional element recited is using Natural Language processing and this additional element is nothing more than instructions to apply the mental process using general-purpose software and hardware.
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.
Claim [ 1, 7, 13 ] are rejected under 35 U.S.C. 102(a)(2) as being unpatentable over Hranj (US12670198 B2, hereinafter Hranj).
Regarding claim 1, Hranj teaches
A computer-implemented method (CIM) comprising: receiving a first historical reading data set for a first user;
[Column 1, lines 24-26 "Examples of the present disclosure describe systems and methods for providing textual summaries based on personalized prior knowledge"];
[Column 9, lines 59-64 "At operation 208, the semantic embeddings for the document and the context for the user request are provided to a knowledge system that has been personalized for the user, such as personal knowledge system 114. In some examples, the knowledge system also receives knowledge information for the user that submitted the user request"];
[Column 3, lines 45-47 " If the received user request is for a summary of an entity, information relating to the entity is collected from one or more data stores"];
[Column 6, lines 58-60 " User knowledge base 110 stores user data relating to previously collected user signals and existing user knowledge of a user (collectively referred to as “knowledge data”)"];
determining a plurality of reader attribute values of a user reading profile for the first user, with the attribute values of the user reading profile for the first user including a first reading level attribute value that indicates a level of knowledge of the first user with respect to a first topic based on the first historical reading data set;
[Column 9, lines 24-39 "At operation 204, a context for the user request is identified. In examples, the context for the user request is identified using information associated with the user that submitted the user request ( e.g., behavioral tendencies, configuration settings/user preferences, and user knowledge objectives), information associated with the user device of the user ( e.g., geographical location data, destination data, and user device capabilities), and information associated with the document for which the document summary was requested ( e.g., required or average reading time, reading complexity, and the topics discussed in the document).Such information may be collected from data sources stored in the computing environment and/or the user device, such as a user profile, event logs, application data, device sensor data, the document, and a data store comprising metadata or an index of the document."];
[Column 3, lines 57-65 "The semantic embeddings, the context for the user request, and information associated with an existing knowledge base of the user are provided as input to a personal knowledge system that is personalized for the user. Non-exhaustive examples of information associated with an existing knowledge base of a user include the user's areas of interest or expertise, the level of the user's interest or knowledge in various knowledge areas or with various documents, documents interactions by the user (e.g., documents or sections of documents the user authored, read, modified, commented on, received, sent, or referred to), electronic communications of the user, text or voice data from meetings and events attended by the user, video and image data associated with the user or interacted with by the user, and other user signals (e.g., detected events) associated with the user."];
receiving a request for a natural language summary, for the first user, of an original text written in natural language text and relating to the first topic;
[Column 9, lines 11-13 "Method 200 begins at operation 202, where a user request for a document summary is received"];
[Column 9, lines 24-34 "In examples, the context for the user request is identified using information associated with the user that submitted the user request (e.g., behavioral tendencies, configuration settings/user preferences, and user knowledge objectives), information associated with the user device of the user (e.g., geographical location data, destination data, and user device capabilities), and information associated with the document for which the document summary was requested (e.g., required or average reading time, reading complexity, and the topics discussed in the document)"].
responsive to the receipt of the request, preparing a first summary of the original text in accordance with the attribute values of the user reading profile for the first user; and
[Column 9, lines 40-41 "At operation 206, semantic embeddings are generated for the document for which the document summary was requested"];
[Column 9 , lines 59-67, Column 10, lines 1-12 "At operation 208, the semantic embeddings for the document and the context for the user request are provided to a knowledge system that has been personalized for the user, such as personal knowledge system 114. In some examples, the knowledge system also receives knowledge information for the user that submitted the user request. The knowledge information comprises previously collected user signals and existing user knowledge of the user. The knowledge information is retrieved from a data store, such as user knowledge base 110. The knowledge system compares the semantic embeddings to the knowledge information to determine similarities (or dissimilarities) between the semantic embeddings and the knowledge information. The similarities (or dissimilarities) identify whether the user is knowledgeable regarding or has previous experience with the information (e.g., topics or facts) represented by a semantic
embedding. As one example, if a semantic embedding is generated for a topic for which the user is an expert, the comparison by the knowledge system will indicate a close similarity (or match) between the semantic embedding and the knowledge information."].
presenting the first summary on a computer device of the first user.
[Column 10, lines 13-14 "At operation 210, a summarization determination is generated for the document"];
[Column 8, lines 65-67 "The summary may then be displayed on or by user device(s) 102 in response to the user request."].
Regarding claim 7, Hranj teaches
A computer program product (CPP) comprising: a set of storage device(s); and
computer code stored collectively in the set of storage device(s), with the computer code including data and instructions to cause a processor(s) set to perform at least the following operations:
[Column 14, lines 7-12 “The term computer readable media as used herein may include computer storage media. Computer storage media may include volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information, such as computer readable instructions, data structures, or program modules”].
receiving a first historical reading data set for a first user;
[Column 1, lines 24-26 "Examples of the present disclosure describe systems and methods for providing textual summaries based on personalized prior knowledge"];
[Column 9, lines 59-64 "At operation 208, the semantic embeddings for the document and the context for the user request are provided to a knowledge system that has been personalized for the user, such as personal knowledge system 114. In some examples, the knowledge system also receives knowledge information for the user that submitted the user request"];
[Column 3, lines 45-47 " If the received user request is for a summary of an entity, information relating to the entity is collected from one or more data stores"];
[Column 6, lines 58-60 " User knowledge base 110 stores user data relating to previously collected user signals and existing user knowledge of a user (collectively referred to as “knowledge data”)"];
determining a plurality of reader attribute values of a user reading profile for the first user, with the attribute values of the user reading profile for the first user including a first reading level attribute value that indicates a level of knowledge of the first user with respect to a first topic based on the first historical reading data set;
[Column 9, lines 24-39 "At operation 204, a context for the user request is identified. In examples, the context for the user request is identified using information associated with the user that submitted the user request ( e.g., behavioral tendencies, configuration settings/user preferences, and user knowledge objectives), information associated with the user device of the user ( e.g., geographical location data, destination data, and user device capabilities), and information associated with the document for which the document summary was requested ( e.g., required or average reading time, reading complexity, and the topics discussed in the document).Such information may be collected from data sources stored in the computing environment and/or the user device, such as a user profile, event logs, application data, device sensor data, the document, and a data store comprising metadata or an index of the document."];
[Column 3, lines 57-65 "The semantic embeddings, the context for the user request, and information associated with an existing knowledge base of the user are provided as input to a personal knowledge system that is personalized for the user. Non-exhaustive examples of information associated with an existing knowledge base of a user include the user's areas of interest or expertise, the level of the user's interest or knowledge in various knowledge areas or with various documents, documents interactions by the user (e.g., documents or sections of documents the user authored, read, modified, commented on, received, sent, or referred to), electronic communications of the user, text or voice data from meetings and events attended by the user, video and image data associated with the user or interacted with by the user, and other user signals (e.g., detected events) associated with the user."];
receiving a request for a natural language summary, for the first user, of an original text written in natural language text and relating to the first topic;
[Column 9, lines 11-13 "Method 200 begins at operation 202, where a user request for a document summary is received"];
[Column 9, lines 24-34 "In examples, the context for the user request is identified using information associated with the user that submitted the user request (e.g., behavioral tendencies, configuration settings/user preferences, and user knowledge objectives), information associated with the user device of the user (e.g., geographical location data, destination data, and user device capabilities), and information associated with the document for which the document summary was requested (e.g., required or average reading time, reading complexity, and the topics discussed in the document)"].
responsive to the receipt of the request, preparing a first summary of the original text in accordance with the attribute values of the user reading profile for the first user; and
[Column 9, lines 40-41 "At operation 206, semantic embeddings are generated for the document for which the document summary was requested"];
[Column 9 , lines 59-67, Column 10, lines 1-12 "At operation 208, the semantic embeddings for the document and the context for the user request are provided to a knowledge system that has been personalized for the user, such as personal knowledge system 114. In some examples, the knowledge system also receives knowledge information for the user that submitted the user request. The knowledge information comprises previously collected user signals and existing user knowledge of the user. The knowledge information is retrieved from a data store, such as user knowledge base 110. The knowledge system compares the semantic embeddings to the knowledge information to determine similarities (or dissimilarities) between the semantic embeddings and the knowledge information. The similarities (or dissimilarities) identify whether the user is knowledgeable regarding or has previous experience with the information (e.g., topics or facts) represented by a semantic
embedding. As one example, if a semantic embedding is generated for a topic for which the user is an expert, the comparison by the knowledge system will indicate a close similarity (or match) between the semantic embedding and the knowledge information."].
presenting the first summary on a computer device of the first user.
[Column 10, lines 13-14 "At operation 210, a summarization determination is generated for the document"];
[Column 8, lines 65-67 "The summary may then be displayed on or by user device(s) 102 in response to the user request."].
Regarding claim 13 , Hranj teaches
A computer system (CS) comprising: a processor(s) set; a set of storage device(s); and
computer code stored collectively in the set of storage device(s), with the computer code including data and instructions to cause the processor(s) set to perform at least the following operations:
[Column 14, lines 7-12 “The term computer readable media as used herein may include computer storage media. Computer storage media may include volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information, such as computer readable instructions, data structures, or program modules”];
[Column 14, lines 63-67 “As will be understood from the foregoing disclosure, one example of the present disclosure relates to a system comprising: a processor; and memory coupled to the processor, the memory comprising computer executable instructions that, when executed by the processor, perform operations’].
receiving a first historical reading data set for a first user;
[Column 1, lines 24-26 "Examples of the present disclosure describe systems and methods for providing textual summaries based on personalized prior knowledge"];
[Column 9, lines 59-64 "At operation 208, the semantic embeddings for the document and the context for the user request are provided to a knowledge system that has been personalized for the user, such as personal knowledge system 114. In some examples, the knowledge system also receives knowledge information for the user that submitted the user request"];
[Column 3, lines 45-47 " If the received user request is for a summary of an entity, information relating to the entity is collected from one or more data stores"];
[Column 6, lines 58-60 " User knowledge base 110 stores user data relating to previously collected user signals and existing user knowledge of a user (collectively referred to as “knowledge data”)"];
determining a plurality of reader attribute values of a user reading profile for the first user, with the attribute values of the user reading profile for the first user including a first reading level attribute value that indicates a level of knowledge of the first user with respect to a first topic based on the first historical reading data set;
[Column 9, lines 24-39 "At operation 204, a context for the user request is identified. In examples, the context for the user request is identified using information associated with the user that submitted the user request ( e.g., behavioral tendencies, configuration settings/user preferences, and user knowledge objectives), information associated with the user device of the user ( e.g., geographical location data, destination data, and user device capabilities), and information associated with the document for which the document summary was requested ( e.g., required or average reading time, reading complexity, and the topics discussed in the document).Such information may be collected from data sources stored in the computing environment and/or the user device, such as a user profile, event logs, application data, device sensor data, the document, and a data store comprising metadata or an index of the document."];
[Column 3, lines 57-65 "The semantic embeddings, the context for the user request, and information associated with an existing knowledge base of the user are provided as input to a personal knowledge system that is personalized for the user. Non-exhaustive examples of information associated with an existing knowledge base of a user include the user's areas of interest or expertise, the level of the user's interest or knowledge in various knowledge areas or with various documents, documents interactions by the user (e.g., documents or sections of documents the user authored, read, modified, commented on, received, sent, or referred to), electronic communications of the user, text or voice data from meetings and events attended by the user, video and image data associated with the user or interacted with by the user, and other user signals (e.g., detected events) associated with the user."];
receiving a request for a natural language summary, for the first user, of an original text written in natural language text and relating to the first topic;
[Column 9, lines 11-13 "Method 200 begins at operation 202, where a user request for a document summary is received"];
[Column 9, lines 24-34 "In examples, the context for the user request is identified using information associated with the user that submitted the user request (e.g., behavioral tendencies, configuration settings/user preferences, and user knowledge objectives), information associated with the user device of the user (e.g., geographical location data, destination data, and user device capabilities), and information associated with the document for which the document summary was requested (e.g., required or average reading time, reading complexity, and the topics discussed in the document)"].
responsive to the receipt of the request, preparing a first summary of the original text in accordance with the attribute values of the user reading profile for the first user; and
[Column 9, lines 40-41 "At operation 206, semantic embeddings are generated for the document for which the document summary was requested"];
[Column 9 , lines 59-67, Column 10, lines 1-12 "At operation 208, the semantic embeddings for the document and the context for the user request are provided to a knowledge system that has been personalized for the user, such as personal knowledge system 114. In some examples, the knowledge system also receives knowledge information for the user that submitted the user request. The knowledge information comprises previously collected user signals and existing user knowledge of the user. The knowledge information is retrieved from a data store, such as user knowledge base 110. The knowledge system compares the semantic embeddings to the knowledge information to determine similarities (or dissimilarities) between the semantic embeddings and the knowledge information. The similarities (or dissimilarities) identify whether the user is knowledgeable regarding or has previous experience with the information (e.g., topics or facts) represented by a semantic
embedding. As one example, if a semantic embedding is generated for a topic for which the user is an expert, the comparison by the knowledge system will indicate a close similarity (or match) between the semantic embedding and the knowledge information."].
presenting the first summary on a computer device of the first user.
[Column 10, lines 13-14 "At operation 210, a summarization determination is generated for the document"];
[Column 8, lines 65-67 "The summary may then be displayed on or by user device(s) 102 in response to the user request."]
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.
Claim [ 2, 3, 6, 8, 9, 12, 14, 15, 18 ] are rejected under 35 U.S.C. 103 as being unpatentable over Hranj (US 12670198 B2, hereinafter Hranj) in view of Rowe (US20060166174A1, hereinafter Rowe).
Regarding claim 2, the rejection of claim 1 is incorporated.
Hranj teaches
The CIM of claim 1 further comprising:
subsequent to the presentation of the first summary, receiving a second historical reading data set for a first user;
[Column 8, lines 61-62 " Summarization engine 116 provides summaries to user device(s) 102 to fulfill user requests for the summaries"];
[Column 3, lines 57-65 "The semantic embeddings, the context for the user request, and information associated with an existing knowledge base of the user are provided as input to a personal knowledge system that is personalized for the user. Non-exhaustive examples of information associated with an existing knowledge base of a user include the user's areas of interest or expertise, the level of the user's interest or knowledge in various knowledge areas or with various documents, documents interactions by the user (e.g., documents or sections of documents the user authored, read, modified, commented on, received, sent, or referred to), electronic communications of the user, text or voice data from meetings and events attended by the user, video and image data associated with the user or interacted with by the user, and other user signals (e.g., detected events) associated with the user."];
[Column 6, lines 58-67, Column 7, lines 1-18 "User knowledge base 110 stores user data relating to previously collected user signals and existing user knowledge of a user (collectively referred to as “knowledge data”). User knowledge base 110 may represent or be stored in a data store, such as a database, a table, or a similar storage device or system. User knowledge base 110 aggregates user data from various data sources (e.g., signal detector 108, the user's application data, data stores external to system 100) and stores the aggregated user data in one or more data structures. As one example, knowledge base 110 comprises a personalized knowledge graph (or any other type of ontologically based data structure) for each user that is a member of system 100. In this example, the personalized knowledge graph comprises objects (e.g., documents, document parts, entities) interacted with by the user, relationships between the objects, and metadata associated with the objects and relationships (e.g., creation date, modification date, most recent interaction date, document properties, and entity properties). The personalized knowledge graph may also comprise weights or scores that are assigned to the objects based on various factors, such as the date/time the object was added to the personalized knowledge graph, the user's expertise or familiarity with the object, the user's interest in the object, the most recent modification of the object, the frequency of the user's interaction with the object, the total number of user interactions with the object, the user's most recent interaction with the object, the type of interaction(s) with the object, and so on." where user knowledge base stores and aggregates previously collected data and maintains time-varying values including most recent interaction, etc indicating that additional user interaction data is received and incorporated into the user's stored historical data over time and this personal knowledge system( user documents read, modified, etc) is used to determine the information and summarization scope for a subsequent generated summary];
Hranj does not teach
performing incremental concept progression on the second historical reading data set to refine the attribute values of the user reading profile.
However, Rowe teaches
performing incremental concept progression on the second historical reading
data set to refine the attribute values of the user reading profile.
[0029 "The present invention uses technology to teach students how to read based on the above cognitive model"];
[0031 "FIG. 2 shows the Student Cognitive Model for the present invention learning system. This Student Cognitive Model represents all the student information that the system can use to customize the learning experience on a per-student basis. This Student Cognitive Model is used, and contributed to by the AI Engine, the Helper and Content Agents, input from a parent and teacher, and by student knowledge information from other sources. The Student Cognitive Model is comprised of a number of modules which we will now describe:
[0032 "The ‘Student Cognitive Model” is the implementation of the students cognitive state and contains the initial student profile as input by parents and teachers, the student's learning style information, and a knowledge base of any other general information where the system might learn about what the student already knows"];
[0033 "The ‘Lesson Task Model/Results’ module represents essentially what the present invention learning system tracks on a per-lesson basis—where the student has progressed to in a particular lesson, difficulties they have had, time spent, number of correct responses, etc. Each task model would continue to be built on a lesson-by-lesson basis, depending on the structure and needs of each lesson" where lesson by lesson task corresponds to historical reading data because it accumulates the user's progress, difficulties, time spent, and responses over multiple lessons.];
[0035 "Finally, the ‘Student Concept Map’ module contains a partially ordered set of “concept nodes”, where each concept node represents one of the abstract concepts that the lessons teach to the student. Each concept node contains a numeric rating of the student's mastery of that concept, and references to lesson entries in the Lesson Task Models/Results module which provide evidence for those ratings."]
[0086 "This information is communicated to the Helper Agent who then updates the Student Cognitive Model with new information about the Student." where subsequently accumulated lesson data including progress, time spent, and responses- as evidences for updating mastery values in the Student cognitive model thereby shows teaching incremental concept progression that refines user profile attribute values];
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Hranj with Rowe because Hranj relies on the user’s knowledge and reading history to personalize summaries, while Rowe provides a technique for keeping such knowledge values current as additional reading data is received. This combination would predictably produce more accurately personalized subsequent summaries. Both systems maintain computer based user specific knowledge models containing values associated with user’s knowledge and therefore using Rowe’s progress, time spent, and response data could be used to update Hranj’s values showing the user’s knowledge of specific topics.
Regarding claim 3, the rejection of claim 2 is incorporated.
Hranj does not teach
The CIM of claim 2 wherein the performance of concept progression refines at
least the first reading level attribute.
But Rowe teaches
The CIM of claim 2 wherein the performance of concept progression refines at least the first reading level attribute.
[0029 "The present invention uses technology to teach students how to read based on the above cognitive model"];
[0035 "Finally, the ‘Student Concept Map’ module contains a partially ordered set of “concept nodes”, where each concept node represents one of the abstract concepts that the lessons teach to the student. Each concept node contains a numeric rating of the student's mastery of that concept, and references to lesson entries in the Lesson Task Models/Results module which provide evidence for those ratings….. As the student works within the learning system, the system gathers additional evidence (the student's performance on lessons designed for a visual learning style) to confirm or modify that predictive classification. The more consistent evidence the system gathers, the higher the certainty rating becomes."]
[0057 “Students' demographic data include, for example, the student's age, enrolled grade, prior reading level, diagnosed special needs, family structure, income level, and type of community and school. Data about students' instructional preferences include their preferred learning style(s) and instructional structure”];
[0087 ” Once the student begins the program the Agents then take over the role of dynamically refining the cognitive model and adapting the program to the needs of the student…. This further helps to refine the Student Cognitive Model on an ongoing basis.]
[0086 "This information is communicated to the Helper Agent who then updates the Student Cognitive Model with new information about the Student." where Rowe uses its cognitive model to teach reading and updates numeric mastery for individual concepts based on student's lesson results, thereby refining a reading level attribute representing the student's knowledge of a particular reading topic or concept];
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Hranj with Rowe because Hranj personalizes summaries based on the user's knowledge of particular topics, while Rowe provides a known way to keep such topic knowledge values current. Applying that process to Hranj would predictably improve later summaries by matching them to the user's current knowledge of the topic.
Regarding claim 6, Hranj teaches
The CIM of claim 2 wherein the performance of concept progression uses
Natural Language Processing relative to a multiplicity of topics by capturing user inputs and behavior of the first user.
[Column 5, lines 13-17 "User device(s) 102 are configured to detect and/or collect input data from one or more users or user devices. In some examples, the input data corresponds to user interaction with one or more software applications or services implemented by, or accessible to, user device(s) 102…..The input data includes, for example, audio input, touch input, text-based input, gesture input, image input, and/or corresponding user signals."];
[Column 6, lines 38 - 41"Such information is based on current user behavior or location, previous user behavior, user preferences or settings configured for the user, or a stated user objective for a user request."];
[Column 6, lines 58- 60 "User knowledge base 110 stores user data relating to previously collected user signals and existing user knowledge of a user (collectively referred to as “knowledge data”)."];
[Column 7, lines 3- 9"In this example, the personalized knowledge graph comprises objects (e.g., documents, document parts, entities) interacted with by the user, relationships between the objects, and metadata associated with the objects and relationships (e.g., creation date, modification date, most recent interaction date, document properties, and entity properties). "];
[Column 7, lines 26- 33 "Embedding engine 112 separates the document or object into one or more segments using, for example, a data parsing utility. A semantic embedding is then generated for each of the segments using an embedding model, such as Bidirectional Encoder Representations of Transformers (BERT), Sentence BERT (SBERT), Principal Component Analysis (PCA), Singular Value Decomposition (SVD), and Word2Vec"];
[Column 8, lines 50-56 "In examples, summarization engine 116 implements an ML language model that generates human-like text from semantic embeddings, such as Generative Pre-trained Transformer 3 (GPT-3), Language Model from Dialogue Applications (LaMDA), and BigScience Large Open-science Open-access Multilingual language model (BLOOM)."];
[Column 3, lines 57- 67 "The semantic embeddings, the context for the user request, and information associated with an existing knowledge base of the user are provided as input to a personal knowledge system that is personalized for the user. Non-exhaustive examples of information associated with an existing knowledge base of a user include the user's areas of interest or expertise, the level of the user's interest or knowledge in various knowledge areas or with various documents, documents interactions by the user (e.g., documents or sections of documents the user authored, read, modified, commented on, received, sent, or referred to)," where personalized knowledge base includes the user's interest or knowledge levels in various knowledge areas and the user's interaction with documents or document sections that user read. Hranj captures text, audio, gesture and applies NPL to process captured user inputs and behavior relative to multiple topics].
Regarding claim 8, the rejection of claim 7 is incorporated. Claim 8 is substantially the same as claim 2 and is therefore rejected under the same rationale as above.
Regarding claim 9, the rejection of claim 8 is incorporated. Claim 9 is substantially the same as claim 3 and is therefore rejected under the same rationale as above.
Regarding claim 12, the rejection of claim 8 is incorporated. Claim 12 is substantially the same as claim 6 and is therefore rejected under the same rationale as above.
Regarding claim 14, the rejection of claim 13 is incorporated. Claim 14 is substantially the same as claim 2 and is therefore rejected under the same rationale as above.
Regarding claim 15, the rejection of claim 14 is incorporated. Claim 15 is substantially the same as claim 3 and is therefore rejected under the same rationale as above.
Regarding claim 18, the rejection of claim 14 is incorporated. Claim 18 is substantially the same as claim 6 and is therefore rejected under the same rationale as above.
Claim [ 4, 5, 10, 11, 16, 17 ] are rejected under 35 U.S.C. 103 as being unpatentable over Hranj (US 12670198 B2, hereinafter Hranj) in view of Rowe (US20060166174A1, hereinafter Rowe) and in further view of Dodelson (US20140193796, hereinafter Dodelson).
Regarding claim 4, the rejection of claim 2 is incorporated.
Hranj in view of Rowe do not teach
The CIM of claim 2 wherein the performance of concept progression refines at least a second reading level attribute reflecting general reading comprehension abilities of the first user.
But Dodelson teaches the CIM of claim 2 wherein the performance of concept progression refines at least a second reading level attribute reflecting general reading comprehension abilities of the first user.
[0050 "At step 208, the system 100 assesses the skill level of the user(s) in one or more subject matters. To perform this step, the system 100 may, for example, deliver a set of questions to the user(s) in different subject matters, such as literacy, reading comprehension, vocabulary, and mathematics, and assess a skill level in each subject area based on a predetermined skill-level scale. For example, the system 100 may use the LEXILE Framework to assess a reading level associated with a number of users. The system 100 would then assign a LEXILE reading score to each user"];
[0051 "At step 210, the system 100 maps the assessed skill level(s) to each user's associated profile generated in step 206"];
[0061 "As each user completes each lesson exercise, the system 100 receives each user's inputs at step 226 and begins to dynamically re-assess the skill level(s) of each user."];
[0062 "Once each user completes the lesson exercise(s), each user may submit the completed lesson exercise(s) for grading. Grading may be performed by the system 100, as in step 228, and the results may be stored in a database and associated with each user's profile."];
[0064 "Once the completed lesson exercise(s) have been graded in step 228, the system 100 may determine whether an adjustment should be made to a user's skill level(s) based upon the completed lesson exercise(s), in step 234. If the system 100 determines the skill level assessed from the completed lesson is the same as the previous skill level of the user, in step 236, the system 100 does not adjust the skill level(s) associated with the user."];
[0065 "If the system 100 determines the skill level assessed from the completed lesson exercise(s) is different than the previous skill level of the user, the system 100 adjusts the appropriate skill level(s) of the user, at step 238. The system 100 is capable of providing continuous re-assessment of the user's skill level(s)." where assessment is done and it stores a user's reading comprehension skill level in the user profile and dynamically adjusts that level based on subsequent lesson results, thereby refining a reading level attribute reflecting user's general reading comprehension ability];
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Hranj in view Rowe because topic knowledge alone may not show whether a user can understand vocabulary and sentence structure of a summary. Dodelson provides a known technique for assessing a user’s general reading comprehension skill based on later lesson results. Using that updated comprehension level together with Hiranj's knowledge profile and Rowe's concept progression process would predictably produce summaries better matched to both what the user knows and what the user can generally understand.
Regarding claim 5, the rejection of claim 2 is incorporated.
Hranj in view of Rowe do not teach
The CIM of claim 2 wherein the performance of concept progression
refines at least a second reading level attribute reflecting vocabulary level of the first user.
But Dodelson teaches the CIM of claim 2 wherein the performance of concept progression refines at least a second reading level attribute reflecting vocabulary level of the first user.
[0050 "At step 208, the system 100 assesses the skill level of the user(s) in one or more subject matters. To perform this step, the system 100 may, for example, deliver a set of questions to the user(s) in different subject matters, such as literacy, reading comprehension, vocabulary, and mathematics, and assess a skill level in each subject area based on a predetermined skill-level scale. For example, the system 100 may use the LEXILE Framework to assess a reading level associated with a number of users. The system 100 would then assign a LEXILE reading score to each user"]
[0051 "At step 210, the system 100 maps the assessed skill level(s) to each user's associated profile generated in step 206"];
[0061 "As each user completes each lesson exercise, the system 100 receives each user's inputs at step 226 and begins to dynamically re-assess the skill level(s) of each user."];
[0062 "Once each user completes the lesson exercise(s), each user may submit the completed lesson exercise(s) for grading. Grading may be performed by the system 100, as in step 228, and the results may be stored in a database and associated with each user's profile."];
[0064 "Once the completed lesson exercise(s) have been graded in step 228, the system 100 may determine whether an adjustment should be made to a user's skill level(s) based upon the completed lesson exercise(s), in step 234. If the system 100 determines the skill level assessed from the completed lesson is the same as the previous skill level of the user, in step 236, the system 100 does not adjust the skill level(s) associated with the user."];
[0065 "If the system 100 determines the skill level assessed from the completed lesson exercise(s) is different than the previous skill level of the user, the system 100 adjusts the appropriate skill level(s) of the user, at step 238. The system 100 is capable of providing continuous re-assessment of the user's skill level(s)." where assessment is done and it stores a user's reading comprehension skill level in the user profile and dynamically adjusts that level based on subsequent lesson results, thereby refining a reading level attribute reflecting user's general reading comprehension ability];
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Hranj in view Rowe because topic knowledge alone may not show whether a user can understand vocabulary and sentence structure of a summary. Dodelson provides a known technique for assessing a user’s vocabulary skill based on later lesson results. Using that updated level together with Hiranj's knowledge profile and Rowe's concept progression process would predictably produce summaries better matched to both what the user knows and what the user can generally understand.
Regarding claim 10, the rejection of claim 8 is incorporated. Claim 10 is substantially the same as claim 4 and is therefore rejected under the same rationale as above.
Regarding claim 11, the rejection of claim 8 is incorporated. Claim 11 is substantially the same as claim 5 and is therefore rejected under the same rationale as above.
Regarding claim 16, the rejection of claim 14 is incorporated. Claim 16 is substantially the same as claim 4 and is therefore rejected under the same rationale as above.
Regarding claim 17, the rejection of claim 14 is incorporated. Claim 17 is substantially the same as claim 5 and is therefore rejected under the same rationale as above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHEZA ABDUL AZIZ whose telephone number is (571)272-9610. The examiner can normally be reached Monday-Friday 7:30am-5pm Alternate Fridays off.
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/SHEZA ABDUL AZIZ/Examiner, Art Unit 2657
/DANIEL C WASHBURN/Supervisory Patent Examiner, Art Unit 2657
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