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 .
Claims 1 – 20 are pending.
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 – 3, 6 – 13, and 18 – 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more
The claim(s) 1, 9 and 18 recite(s)
“query a data collection with a zero-vector to retrieve a subset of data from the data collection; and generate, using a machine learning model, a summary of the data collection based at least on the subset of data.”
and
“query a data collection with a vector to retrieve a subset of data from the data collection, the vector comprising a plurality of zeros and having a dimension corresponding to a dimension of one or more embeddings associated with the data of the collection; and generate, using a machine learning model, a summary of the data collection based at least on the subset of data”
Step 1:
The claims as a whole fall within one or more statutory categories.
Step 2A prong 1:
The following limitations are identified as abstract idea(s):
“generate a summary of the data collection based at least on the subset of data”
Which is directed to a mental process including observation, evaluation, judgement and opinion.
Step 2A prong 2:
This judicial exception is not integrated into a practical application because the elements including query a data collection with a zero-vector to retrieve a subset of data from the data collection is directed to adding insignificant extra-solution activity to the abstract idea, see MPEP 2106.05(g), such as mere data gathering.
Step 2B:
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claim elements such as processing circuitry is directed to mere use of a computer as a tool to perform the abstract idea, see MPEP 2106.05(f), and machine learning model is directed to generally linking the use of the abstract idea to a particular technological environment or field of use, see MPEP 2106.05(h).
The claim(s) 2, 10 and 19 recite(s) “receive one or more prompts; select, using the machine learning model, the data collection based at least on the summary of the data collection and the one or more prompts; generate, using the machine learning model, an answer to the one or more prompts based at least on the selected data collection; and display, via a user interface, the answer to the one or more prompts”.
Step 1:
The claims as a whole fall within one or more statutory categories.
Step 2A prong 1:
The following limitations are identified as abstract idea(s):
“select the data collection based at least on the summary of the data collection and the one or more prompts” and “generate an answer to the one or more prompts based at least on the selected data collection”
Which are directed to a mental process including observation, evaluation, judgement and opinion.
Step 2A prong 2:
This judicial exception is not integrated into a practical application because the elements such as “receive one or more prompts” and “display the answer to the one or more prompts” which are directed to adding insignificant extra-solution activity to the abstract idea, see MPEP 2106.05(g), such as mere data gathering and presentation.
Step 2B:
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements such as “processing circuitry” is directed to mere use of a computer as a tool to perform the abstract idea, see MPEP 2106.05(f), and “machine learning model” and “user interface” are directed to generally linking the use of the abstract idea to a particular technological environment or field of use, see MPEP 2106.05(h).
The claim(s) 3 and 11 recite similar abstract idea and additional elements as that of claims 1 and 9 above, and are rejected using similar rationale.
The claim(s) 6 and 12 recite(s) “wherein the summary is a single-sentence summary”.
Step 1:
The claims as a whole fall within one or more statutory categories.
Step 2A prong 1:
The following limitations are identified as abstract idea(s):
See claim 1 above.
Step 2A prong 2:
This judicial exception is not integrated into a practical application because the element of “wherein the summary is a single-sentence summary” is directed to adding insignificant extra-solution activity to the abstract idea, see MPEP 2106.05(g), such as data type and format.
Step 2B:
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, see claim 1 above.
The claim(s) 7 and 13 recite(s) “wherein the summary is a plurality of sentences”.
Step 1:
The claims as a whole fall within one or more statutory categories.
Step 2A prong 1:
The following limitations are identified as abstract idea(s):
See claim 1 above.
Step 2A prong 2:
This judicial exception is not integrated into a practical application because the element “wherein the summary is a plurality of sentences” is directed to adding insignificant extra-solution activity to the abstract idea, see MPEP 2106.05(g), such as data type and format.
Step 2B:
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, see claim 1 above.
The claim(s) 8 and 20 recite(s) “wherein the processing circuitry is comprised in at least one of: a control system for an autonomous or semi-autonomous machine; a perception system for an autonomous or semi-autonomous machine; a system for performing simulation operations; a system for performing digital twin operations; a system for performing light transport simulation; a system for performing collaborative content creation for 3D assets; a system for performing deep learning operations; a system for performing remote operations; a system for performing real-time streaming; a system for generating or presenting one or more of augmented reality content, virtual reality content, or mixed reality content; a system implemented using an edge device; a system implemented using a robot; a system for performing conversational Al operations; a system implementing one or more multi-model language models; a system implementing one or more large language models (LLMs); a system implementing one or more large language models (SLMs); a system implementing one or more vision language models (VLMs); a system for generating synthetic data; a system for generating synthetic data using Al; a system incorporating one or more virtual machines (VMs); a system using or deploying one or more inference microservices; a system that incorporates one or more machine learning models deployed in a service or microservice along with an OS-level virtualization package; a system implemented at least partially in a data center; or a system implemented at least partially using cloud computing resources”.
Step 1:
The claims as a whole fall within one or more statutory categories.
Step 2A prong 1:
The following limitations are identified as abstract idea(s):
See claim 1 above.
Step 2A prong 2:
This judicial exception is not integrated into a practical application because the elements are directed to generally linking the use of the abstract idea to a particular technological environment or field of use, see MPEP 2106.05(h).
Step 2B:
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements are directed to generally linking the use of the abstract idea to a particular technological environment or field of use, see MPEP 2106.05(h).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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(s) 1 – 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2024/0289863 A1 issued to Smith Lewis et al (hereinafter Smith) in view of US 2023/0195809 A1 issued to Thonet et al (hereinafter Thonet).
As to claim 1, Smith discloses a system comprising processing circuitry to:
query a data collection with a vector to retrieve a subset of data from the data collection (content, including conversation histories of users, is ingested and embedded by way of vectors in the knowledge base, see Smith: Para. 0019, 0045 – 0050, and initial query is preprocessed into a resulting vector and used to search the database for identifying similar vectors in the database (i.e. of conversation history), generating a range of possible questions that chunks of the conversation would be important for answering and embedding the summaries, questions, and chunks, and indexing the vectors thereafter, see Smith: Para. 0048 – 0053); and
generate, using a machine learning model, a summary of the data collection based at least on the subset of data (summarizing meaning of full conversations in the conversation history and grouping conversation histories, see Smith: Para. 0076 and 0099).
However, Smith does not explicitly disclose query a data collection with a zero-vector to retrieve a subset of data from the data collection.
Thonet teaches query a data collection with a zero-vector to retrieve a subset of data from the data collection (base embedding may include a zero vector, see Thonet: Para. 0019, 0032, 0078, and using base embedding to query data layers and datasets, see Thonet: Para. 0019, 0027, 0032 – 0034, 0050, 0101 – 0114).
Smith and Thonet are analogous due to their disclosure of the use of embedding vectors for storage and retrieval of data collections from database.
Therefore, it would have been obvious to one of ordinary skill in the art to modify Smith’s use of query vectors for identifying a subset of collection of data, such as a conversation history, and summarizing the meaning of the data collections with Thonet’s use of a zero vector as the initial base embedding for identifying an initial subset of data in order to unify search and recommendation tasks for personalized search and recommendation, see Thonet: Para. 0012.
As to claim 2, Smith modified by Thonet discloses the system of claim 1, wherein the processing circuitry is to:
receive one or more prompts (search the vector database in response to use query, see Smith: Para. 0052);
select, using the machine learning model, the data collection based at least on the summary of the data collection and the one or more prompts (identify chunks of conversation histories in response to the user query, see Smith: Para. 0052 – 0053, 0075 – 0076, identifying and querying across past conversations to find related issues is select data collections based on the summary of the conversations and user query);
generate, using the machine learning model, an answer to the one or more prompts based at least on the selected data collection (generate a personalized response/answer to user’s query by AI-drive conversation agent, see Smith: Para. 0051 – 0053, 0058 – 0059, 0067, 0074 – 0076, 0081); and
display, via a user interface, the answer to the one or more prompts (provide the response/answer to the user, see Smith: Para. 0051 – 0053, 0058 – 0059, 0067, 0074 – 0076, 0081, 0101).
As to claim 3, Smith modified by Thonet discloses the system of claim 1, wherein the processing circuitry is to:
query a second data collection with a second zero-vector to retrieve a second subset of data from the second data collection (initial query is preprocessed into a resulting vector and used to search the database for identifying similar vectors in the database (i.e. of conversation history), generating a range of possible questions that chunks of the conversation would be important for answering and embedding the summaries, questions, and chunks, and indexing the vectors thereafter, see Smith: Para. 0048 – 0053 and 0074 – 0076, the preprocessing occurs for each conversation in the conversation history of each user); and
generate, using the machine learning model, a second summary of the second data collection based at least on the second subset of data (summarizing meaning of full conversations in each conversation history and grouping conversation histories, see Smith: Para. 0074 – 0076 and 0099).
As to claim 4, Smith modified by Thonet discloses the system of claim 1, wherein the processing circuitry is to:
determine a threshold based at least on a dimension of the machine learning model (tokenize the subset by chunking and splitting into overlapping sections of no more than N characters, see Smith: Para. 0048 – 0050, no more than N characters for max size of a subset chunk is a threshold);
divide the subset of data into a plurality of chunks based at least on a size of the subset of data exceeding the threshold (tokenize the subset by chunking and splitting into overlapping sections of no more than N characters, see Smith: Para. 0048 – 0050, no more than N characters for max size of a subset chunk is a threshold);
input a first prompt to the machine learning model to generate a second summary of each chunk of the plurality of chunks to obtain a plurality of summaries (input chunks through the large language neural network to summarize the meaning of each chunk, see Smith: Para. 0048 – 0051);
input a second prompt to the machine learning model to condense the plurality of summaries into a third summary (entire conversation summarizations are based on the series of messages within the conversation (chunks), see Smith: Para. 0076); and
generate the summary of the data collection based at least on the third summary (entire conversation summarizations are based on the series of messages within the conversation (chunks), see Smith: Para. 0076).
As to claim 5, Smith modified by Thonet discloses the system of claim 1, wherein the processing circuitry is to:
determine a threshold based on a context limit of the machine learning model (tokenize the subset by chunking and splitting into overlapping sections of no more than N characters, sections including complete sentences or paragraphs, see Smith: Para. 0048 – 0050, no more than N characters for max size of a subset chunk is a threshold, complete sentence or paragraphs are context limits);
input the subset of data into a tokenizer to determine a number of tokens of the subset of data (tokenize the subset by chunking and splitting into overlapping sections of no more than N characters, see Smith: Para. 0048 – 0050, number of tokens or characters are determined during the chunking process);
divide the subset of data into a plurality of chunks based at least on the number of tokens exceeding the threshold (tokenize the subset by chunking and splitting into overlapping sections of no more than N characters, see Smith: Para. 0048 – 0050, no more than N characters for max size of a subset chunk is a threshold);
input a first prompt into the machine learning model to generate a second summary of each chunk of the plurality of chunks to obtain a plurality of summaries (input chunks through the large language neural network to summarize the meaning of each chunk, see Smith: Para. 0048 – 0051);
input a second prompt into the machine learning model to condense the plurality of summaries into a third summary (entire conversation summarizations are based on the series of messages within the conversation (chunks), see Smith: Para. 0076); and
generate the summary of the data collection based at least on the third summary (entire conversation summarizations are based on the series of messages within the conversation (chunks), see Smith: Para. 0076).
As to claim 6, Smith modified by Thonet discloses the system of claim 1, wherein the summary is a single-sentence summary (summaries for conversations including snippets of each conversation history, see Smith: Para. 0045, 0051, 0076, 0099 – 0101, 0143, and snippet may be a complete sentence or single paragraph, etc. see Smith: Para. 0048).
As to claim 7, Smith modified by Thonet discloses the system of claim 1, wherein the summary is a plurality of sentences (summaries for conversations including snippets of each conversation history, see Smith: Para. 0045, 0051, 0076, 0099 – 0101, 0143, and snippet may be a complete sentence or single paragraph, etc. see Smith: Para. 0048).
As to claim 8, Smith modified by Thonet discloses the system of claim 1, wherein the processing circuitry is comprised in at least one of:
a control system for an autonomous or semi-autonomous machine; a perception system for an autonomous or semi-autonomous machine; a system for performing simulation operations; a system for performing digital twin operations; a system for performing light transport simulation; a system for performing collaborative content creation for 3D assets; a system for performing deep learning operations; a system for performing remote operations (system distributed across multiple remote sites, see Smith: Para. 0150); a system for performing real-time streaming; a system for generating or presenting one or more of augmented reality content, virtual reality content, or mixed reality content (AI system part of virtual reality platform, see Smith: Para. 0133); a system implemented using an edge device; a system implemented using a robot; a system for performing conversational Al operations (ingesting and embedding data for use with trained AI-driven conversational agents, see Smith: Para. 0005, 0012, 0038, 0110); a system implementing one or more multi-model language models; a system implementing one or more large language models (LLMs) (system comprising LLM for processing, see Smith: Para. 0042, 0072, 0077, 0099); a system implementing one or more large language models (SLMs) (large language model, see Smith: Para. 0041 – 0055); a system implementing one or more vision language models (VLMs); a system for generating synthetic data; a system for generating synthetic data using Al (ingesting and embedding data for use with trained AI-driven conversational agents, see Smith: Para. 0005, 0012, 0038, 0110); a system incorporating one or more virtual machines (VMs); a system using or deploying one or more inference microservices; a system that incorporates one or more machine learning models deployed in a service or microservice along with an OS-level virtualization package; a system implemented at least partially in a data center (data center, see Smith: Para. 0154); or a system implemented at least partially using cloud computing resources (cloud computing system, see Smith: Para. 0154).
Claim 9 is rejected using similar rationale to the rejection of claim 1 above.
Claim 10 is rejected using similar rationale to the rejection of claim 2 above.
Claim 11 is rejected using similar rationale to the rejection of claim 3 above.
Claim 12 is rejected using similar rationale to the rejection of claim 6 above.
Claim 13 is rejected using similar rationale to the rejection of claim 7 above.
Claim 14 is rejected using similar rationale to the rejection of claim 4 above.
Claim 15 is rejected using similar rationale to the rejection of claim 4 above.
Claim 16 is rejected using similar rationale to the rejection of claim 5 above.
Claim 17 is rejected using similar rationale to the rejection of claim 5 above.
As to claim 18, Smith discloses a system comprising one or more processors to:
query a data collection with a vector to retrieve a subset of data from the data collection (content, including conversation histories of users, is ingested and embedded by way of vectors in the knowledge base, see Smith: Para. 0019, 0045 – 0050, and initial query is preprocessed into a resulting vector and used to search the database for identifying similar vectors in the database (i.e. of conversation history), see Smith: Para. 0048 – 0053); and
generate, using a machine learning model, a summary of the data collection based at least on the subset of data (summarizing meaning of full conversations in the conversation history and grouping conversation histories, see Smith: Para. 0076 and 0099).
However, Smith does not explicitly disclose query a data collection with a vector to retrieve a subset of data from the data collection, the vector comprising a plurality of zeros and having a dimension corresponding to a dimension of one or more embeddings associated with the data of the collection.
Thonet teaches query a data collection with a vector to retrieve a subset of data from the data collection, the vector comprising a plurality of zeros and having a dimension corresponding to a dimension of one or more embeddings associated with the data of the collection (base embedding may include a zero vector, see Thonet: Para. 0019, 0032, 0078, and using base embedding to query data layers and datasets, see Thonet: Para. 0019, 0027, 0032 – 0034, 0050, 0101 – 0114).
Smith and Thonet are analogous due to their disclosure of the use of embedding vectors for storage and retrieval of data collections from database.
Therefore, it would have been obvious to one of ordinary skill in the art to modify Smith’s use of query vectors for identifying a subset of collection of data, such as a conversation history, and summarizing the meaning of the data collections with Thonet’s use of a zero vector as the initial base embedding for identifying an initial subset of data in order to unify search and recommendation tasks for personalized search and recommendation, see Thonet: Para. 0012.
Claim 19 is rejected using similar rationale to the rejection of claim 2 above.
Claim 20 is rejected using similar rationale to the rejection of claim 8 above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARK E HERSHLEY whose telephone number is (571)270-7774. The examiner can normally be reached M-F: 9am-6pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Amy Ng can be reached at (571) 270-1698. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MARK E HERSHLEY/Primary Examiner, Art Unit 2164
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