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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. CN202311669084.3, filed on December 6, 2023.
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 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 1 is a system claim thus it falls into one of the four categories of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
Regarding independent claim 1, following limitations recite a judicial exception:
“receiving at least one key word, and vectorizing each of the at least one key word to generate a key vector”
[Mathematical Calculations] –vectorizing keywords requires mathematical computation which recites to an abstract idea.
“transmitting the key vector to the company knowledge base to be compared with the math vectors, and when the key vector matches one of the math vectors, receiving the piece of patent raw data corresponding to the one of the math vectors from the company knowledge base, and integrating and generating a search result, based on the matching”
[Mental Process] – comparing the vectors with the existing vectors to find similar ones to integrate and generate a result involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen
“outputting the search result, and performing labelling on the piece of patent raw data of the search result, to generate at least one label message”
[Mental Process] –labeling the results to generate label involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen
“vectorizing the at least one label message to generate a label vector, and storing the label vector to the company knowledge base as the math vector corresponding to the labelled patent raw data”
[Mathematical Calculations] –vectorizing keywords requires mathematical computation which recites to an abstract idea.
“transmitting the key vector to the company knowledge base to be compared with the math vector, integrating the search result, and outputting the search result, again”
[Mental Process] – comparing the vectors with the existing vectors to find similar ones to integrate and generate a result involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
Regarding Claim 1, the claim recites additional elements of
“receiving”, “transmitting”, “outputting”
These are merely data gathering recited at a high level of generality, thus is insignificant extra-solution activity (See MPEP 2106.05(g)).
“a company knowledge base, configured to store one or more pieces of patent raw data, wherein each of the pieces of patent raw data corresponds to at least one math vector”
The base is recited at a high level of generality and is merely adding words “apply it” to the judicial exception. (see MPEP 2106.05(f))
“a server-end device, linked to the company knowledge base through a network, wherein the server-end device comprises:”
The device is recited at a high level of generality and is merely adding words “apply it” to the judicial exception. (see MPEP 2106.05(f))
“a non-transitory computer-readable storage medium, configured to store one or more computer readable instructions”
The medium is recited at a high level of generality and is merely adding words “apply it” to the judicial exception. (see MPEP 2106.05(d))
“a hardware processor, electrically connected to the non-transitory computer-readable storage medium, and configured to execute the computer readable instructions to make the server-end device execute”
The processor is recited at a high level of generality and is merely adding words “apply it” to the judicial exception. (see MPEP 2106.05(d))
[Even when viewed in combination, the additional elements do no more than automate the mental processes that a person could perform, using computer components as a tool, thus the claim as a whole does not integrate into a practical application.]
Step 2B – whether the claim as a whole amount to significantly more than the judicial exception? I.e. Are there any additional elements (features/limitations/step) recited in the claim beyond the abstract idea?
The claim does not provide an inventive concept (significantly more than the abstract idea). The claim is ineligible.
As explained above, the additional element [1] is considered an insignificant extra solution activity and at best the equivalent of a mere data gathering recited at a high level of generality and amount to receiving or transmitting data over a network, which is well-understood, routine, conventional activity (See MPEP 2106.05(d), subsection II).
The additional elements [2,3] are considered a mere instruction to apply an exception to the generic computer components or machine-learning components that simply run mathematical calculations and mental processes (see MPEP 2106.05(f)). This limitation remains a mere instruction to apply an exception even upon reconsideration. Even when considered in combination, the additional element represents a mere instruction to apply an exception, which cannot provide an inventive concept.
The additional elements [4,5] are considered merely computer components that are just to store and execute code-based instructions which are considered a mere instruction to apply an exception and amount to storing and receiving information in memory, which is well-understood, routine, conventional activity (See MPEP 2106.05(d), subsection II). This limitation remains a mere instruction to apply an exception.
These limitations remain insignificant extra-solution activity and mere instruction to apply an exception even upon reconsideration. Even when considered in combination, the additional element represents insignificant extra-solution activity and a mere instruction to apply an exception, which cannot provide an inventive concept.
Regarding Claim 2
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 2 is a dependent claim of 1, thus it falls within the same category of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
Regarding dependent claim 2, following limitations recite a judicial exception:
“the server-end device receives at least one conversation message, extracts the key word from the conversation message based on a natural language processing technology, and stores at least one of the at least one conversation message and the key word”
[Mental Process] – receive a message and extracting keywords from the conversation to store them involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
Regarding Claim 2, the claim recites additional elements of
“the server-end device”
The device is recited at a high level of generality and is merely adding words “apply it” to the judicial exception. (see MPEP 2106.05(f))
“natural language processing technology”
The technology is recited at a high level of generality and is merely adding words “apply it” to the judicial exception. (see MPEP 2106.05(f))
[Even when viewed in combination, the additional elements do no more than automate the mental processes that a person could perform, using computer components as a tool, thus the claim as a whole does not integrate into a practical application.]
Step 2B – whether the claim as a whole amount to significantly more than the judicial exception? I.e. Are there any additional elements (features/limitations/step) recited in the claim beyond the abstract idea?
The claim does not provide an inventive concept (significantly more than the abstract idea). The claim is ineligible.
As explained above, the additional elements [1, 2] are considered a mere instruction to apply an exception to the generic computer components or machine-learning components that simply run mathematical calculations and mental processes (see MPEP 2106.05(f)). These limitations remain a mere instruction to apply an exception even upon reconsideration. Even when considered in combination, the additional element represents a mere instruction to apply an exception, which cannot provide an inventive concept.
Regarding Claim 3
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 3 is a dependent claim of 2, thus it falls within the same category of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
Regarding dependent claim 3, following limitations recite a judicial exception:
“the search result is labelled by at least one of an automatic manner and a manual manner, and an approximate vocabulary is selected as the label message based on the natural language processing technology”
[Mental Process] – labeling the results and selecting approximate vocabulary involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
Regarding Claim 3, the claim recites additional elements of
“natural language processing technology”
The technology is recited at a high level of generality and is merely adding words “apply it” to the judicial exception. (see MPEP 2106.05(f))
[Even when viewed in combination, the additional elements do no more than automate the mental processes that a person could perform, using computer components as a tool, thus the claim as a whole does not integrate into a practical application.]
Step 2B – whether the claim as a whole amount to significantly more than the judicial exception? I.e. Are there any additional elements (features/limitations/step) recited in the claim beyond the abstract idea?
The claim does not provide an inventive concept (significantly more than the abstract idea). The claim is ineligible.
As explained above, the additional element [1] is considered a mere instruction to apply an exception to the generic computer components or machine-learning components that simply run mathematical calculations and mental processes (see MPEP 2106.05(f)). This limitation remains a mere instruction to apply an exception even upon reconsideration. Even when considered in combination, the additional element represents a mere instruction to apply an exception, which cannot provide an inventive concept.
Regarding Claim 4
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 4 is a dependent claim of 1, thus it falls within the same category of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
Regarding dependent claim 4, following limitations recite a judicial exception:
“one of the pieces of the patent raw data comprises a case status, and wherein when the search result is re-integrated, the case status corresponding to the received piece of patent raw data is embedded in the search result”
[Mental Process] – embedding the case status to the search result is simply adding additional information into the result which involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
The claim 4 does not recite any additional elements other than abstract ideas, so it does not integrate into a practical application. Thus, this claim is directed to the abstract idea.
Regarding Claim 5
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 5 is a dependent claim of 1, thus it falls within the same category of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
Regarding dependent claim 5, following limitations recite a judicial exception:
“one of the pieces of patent raw data comprises an inventor message, and wherein when the search result is re-integrated, the server-end device selects the piece of patent raw data having the same inventor message as the piece of patent raw data received from the company knowledge base, to build an association recommendation, and embeds the association recommendation into the re-integrated search result”
[Mental Process] – selecting patents that have the same inventor message to build a recommendation and embed it into the search result is simply adding additional information into the result by comparing the messages which involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
Regarding Claim 5, the claim recites additional elements of
“a server-end device”
The device is recited at a high level of generality and is merely adding words “apply it” to the judicial exception. (see MPEP 2106.05(f))
[Even when viewed in combination, the additional elements do no more than automate the mental processes that a person could perform, using computer components as a tool, thus the claim as a whole does not integrate into a practical application.]
Step 2B – whether the claim as a whole amount to significantly more than the judicial exception? I.e. Are there any additional elements (features/limitations/step) recited in the claim beyond the abstract idea?
The claim does not provide an inventive concept (significantly more than the abstract idea). The claim is ineligible.
As explained above, the additional elements [1] is considered a mere instruction to apply an exception to the generic computer components or machine-learning components that simply run mathematical calculations and mental processes (see MPEP 2106.05(f)). These limitations remain a mere instruction to apply an exception even upon reconsideration. Even when considered in combination, the additional element represents a mere instruction to apply an exception, which cannot provide an inventive concept.
Regarding Claims 6-10
Claims 6-10 have similar limitations of Claims 1-5, respectively. For the reasons described above with respect to Claims 1-5, these judicial exceptions are not meaningfully integrated into a practical application, or significantly more than the abstract ideas. The claims do not provide anything more than the abstract ideas of mental processes and mathematical calculations that are practically capable of being performed with the assistance of pen and paper. Therefore, Claims 6-10 also recite abstract ideas that do not integrate into a practical application or amount to significantly more than judicial exception, and thus are rejected under U.S.C. 101.
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.
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.
Claims 1-4, 6-9 are rejected under 35 U.S.C. 103 as being unpatentable over Lin et al. (Lin), Chinese Patent Application No. CN-116109447-A, Published in May 2023, in view of Wu et al. (Wu), Non-Patent Literature, “Rethinking Similarity Search: Embracing Smarter Mechanisms over Smarter Data”, Published in August 2023, arXiv, Pages: 6.
As to independent Claim 1,
Lin teaches an extraction system for a corporate knowledge base, comprising:
a company knowledge base, configured to store one or more pieces of patent raw data, wherein each of the pieces of patent raw data corresponds to at least one math vector (Lin, Pg13, Paragraph3, Lines1-3, "the administrator can upload a large number of vectors of design pictures to the vector library of design pictures, and each vector corresponds to a unique ID"
Pg13, Paragraph4, Lines1-2, "the graphs corresponding to the vectors in the design image vector library are synchronized with the design graphs in the public full-text search database ElasticSearch"
Pg14, Step104, "Step S104: Perform a patent search in the full-text search database according to the patent filtering conditions and the patent identifiers corresponding to several similar vectors, and determine multiple target patent information", wherein Lin explicitly discloses the search database EalsticSearch which is designed to store patent information, which the information also comprises a vector of a unique ID (the corresponding patent), rendering it functionally equivalent to the claimed invention); and
a server-end device, linked to the company knowledge base through a network, wherein the server-end device comprises (Lin, Pg11, Paragraph5, Lines1-4, "As shown in Figure 1, a search scene diagram provided by the embodiment of the present application includes: a terminal device and a search device; the terminal device and the search device are connected through a network, such as a wired or wireless network connection, wherein the search The device is set in the search device", wherein Lin explicitly discloses that the search device (the corresponding database) is connected over the network to the terminal device (the corresponding server-end device), rendering it functionally equivalent to the claimed invention):
a non-transitory computer-readable storage medium, configured to store one or more computer readable instructions (Lin, Pg31, Claim10, "10. A computer-readable storage medium on which a computer program is stored, wherein when the computer program is executed in a computer, the computer is made to execute the method according to any one of claims 1-7"); and
a hardware processor, electrically connected to the non-transitory computer-readable storage medium, and configured to execute the computer readable instructions to make the server-end device execute (Lin, Pg30, Claim9, "9. A search device, characterized in that it comprises: at least one processor; memory;"
Pg26, Paragraph3, Lines2-4, "The processor 601 is configured to execute the application program code stored in the memory 603, so as to implement the content shown in the foregoing method embodiments"):
receiving at least one key word, and vectorizing each of the at least one key word to generate a key vector (Lin, Pg3, Paragraph4, "When receiving a picture search request with patent filtering conditions, obtain the picture to be searched;"
Pg12, Step101, Paragraph4, Lines1-3, "Among them, the patent filter conditions include but are not limited to any one or more of the following: applicant, inventor, address, classification number, patent agency, brief description and patent status"
Pg13, Paragraph1, Lines1-3, "the extracted feature vector can represent the characteristics of the object in the image, and specifically can include information of various dimensions, for example: picture information, character information and label information"
Pg19, Paragraph2, "the feature vectors include a first feature vector
corresponding to picture information and a second feature vector corresponding to character information, and each vector in each sub- design picture vector library includes each The first feature vector and the second feature vector corresponding to the sub-design picture"
Pg28, Claim1, "Extracting the feature vectors of the pictures to be searched, and determining some similar vectors corresponding to the feature vectors according to the feature vectors and the design picture vector library", wherein Lin explicitly discloses that the system receives user's picture search request which then the features of the image are extracted. Lin also mentions that it takes text information which it is also vectorized, rendering it functionally equivalent to the claimed invention);
transmitting the key vector to the company knowledge base to be compared with the math vectors, and when the key vector matches one of the math vectors, receiving the piece of patent raw data corresponding to the one of the math vectors from the company knowledge base, and integrating and generating a search result, based on the matching (Lin, Pg28, Claim1, "Extracting the feature vectors of the pictures to be searched, and determining some similar vectors corresponding to the feature vectors according to the feature vectors and the design picture vector library; Determine the patent identifiers corresponding to several similar vectors; According to the patent filter condition and the corresponding patent identifiers of several similar vectors, a patent search is carried out in the full-text search database to determine a plurality of target patent information;"
Pg14, Step104 Paragraph2, Lines3-7, "determine multiple target patents from the patent information according to the patent filter conditions and the similarity values corresponding to the patent information, the obtained target patent information is the patent information that satisfies both the semantic information of the picture and the picture information", wherein Lin explicitly discloses that the vectors created from the user request is then compared to the similar vectors in the database to match the request and output the results, rendering it functionally equivalent to the claimed invention.);
Lin, however, does not teach the following limitations:
outputting the search result, and performing labelling on the piece of patent raw data of the search result, to generate at least one label message;
vectorizing the at least one label message to generate a label vector, and storing the label vector to the company knowledge base as the math vector corresponding to the labelled patent raw data; and
transmitting the key vector to the company knowledge base to be compared with the math vector, integrating the search result, and outputting the search result, again
From the same field of endeavor, Wu teaches these limitations:
outputting the search result, and performing labelling on the piece of patent raw data of the search
result, to generate at least one label message (Wu, Pg4, Section4.1, Paragraph3, Lines2-5, " when the initial similarity search results are presented to the user, they provide feedback on these found examples by labeling them as positive or negative"
Pg4, Section4.2, Lines2-4, "Suppose the similarity search retrieves the top 5 data points, ranked in descending order of similarity: x1,x2,x3,x4,x5. User feedback indicates that x1,x4,x5 are positive results, and x2,x3 are negative", wherein Wu explicitly discloses that after the first initial similarity search, the user actually gives feedback to the result by labeling positive or negative, rendering it equivalent to the claimed invention of labeling to the first results as a feedback to the first search);
vectorizing the at least one label message to generate a label vector, and storing the label vector to the company knowledge base as the math vector corresponding to the labelled patent raw data (Wu, Pg4, Section4.2, Paragraph4, Lines1-3, "Adapting data embedding. This approach is preferable when Adapting data embedding. larger adaptations are required, such as when conducting a series of queries with continuous user feedback"
Pg5, Left Column, First Bullet, Lines1-6, "Parameterized embedding. The embedding of each data point xi is a weighted sum of several high-dimensional embeddings... These weights can be adjusted to account for user feedback"
Pg5, Left Column, Second Bullet, Lines4-5, "...update only the data points that would affect future query results", wherein Wu discloses the pipeline of adapting data embedding, which Wu vectorizes the feedback labels into high-dimensional weight/vector, parameterized embedding, such that this embedding partially update the database according to the feedback for better accuracy, rendering it functionally equivalent to the claimed invention); and
transmitting the key vector to the company knowledge base to be compared with the math vector, integrating the search result, and outputting the search result, again (Wu, Pg4, Section4.1, Paragraph3, Lines4-7, "Based on this feedback, we adapt similarity search parameters (e.g., weights of different embeddings or embedding feature dimensions), and the similarity search results are updated. This process can be repeated multiple times as needed"
Pg2, Section3.2, Lines2-4 and Third Bullet, "The idea is to perform the similarity search in an iterative manner, gradually expanding the query set...", "Repeat until a predefined stopping criterion is met", wherein Wu discloses the iterative search mechanism of updating the database based on the feedback labels to return better results, rendering it functionally equivalent to the claimed invention of retrieving a search result based on the database that has been updated by the newly added labels.)
Lin and Wu are analogous to the claimed invention as they are from the same field of endeavor of information retrieval and similarity search in high-dimensional vector databases, specifically applied to document and patent retrieval systems. Therefore, it would have been obvious to one of the ordinary skills in the art, before the effective filing date, to combine the multimodal patent vector search framework and full-text database querying system of Lin with the Human-in-the-Loop(HITL) iterative feedback mechanism, result labeling, and dynamic data embedding update pipeline of Wu. The motivation is as recited by Wu (Wu, Pg1, Right Column, Opportunity2, Lines1-3 and Lines9-12, "User feedback is a critical resource for improving the accuracy of similarity search results" and "Ideally, we would like to dynamically adapt the similarity search results according to user feedback for a specific query or question domain without updating the entire database") such that the combination allows the system to continuously adapt feature vector embeddings based on user labeling without the computational overhead of retraining the whole model, thereby eliminating false positives in initial query results and dynamically refining the output to precisely capture subtle technical nuances and specific user search intents in the patent domain.
As to dependent Claim 2,
The combination of Lin and Wu teaches, as mentioned above, all the limitations of Claim 1. It teaches the overall architecture of retrieval-augmented generation(RAG) which the main server communicates with the terminal device to receive user's request to a search of a patent, which the search texts are then vectorized to find similar vectors from the database, which then the user adds labels to the result, which are then vectorized to augment or update the database, which then retrieves the search result based on the updated database.
Lin, however, does not teach the following limitations, but from the same field of endeavor Wu teaches the extraction system for a corporate knowledge base according to claim 1, wherein
the server-end device receives at least one conversation message, extracts the key word from the conversation message based on a natural language processing technology, and stores at least one of the at least one conversation message and the key word (Wu, Pg4, Right Column, Conversional Information Retrieval, Lines1-5, "Large Language Models (LLMs) facilitate a conversational style of information access. For example, in order to retrieve a specific image from a large collection, a user could engage in a multi-round conversation with the LLM"
P22, Right Column, Custom Q&A System, Paragraph2, Lines4-6, "During the querying phase, we generate embedding for the user query and perform a similarity search on the vector database"
Pg4, Section4.2, Paragraph3, Lines3-5, "as the user’s feedback naturally updates the query embedding for subsequent responses"
P22, Right Column, Custom Q&A System, Paragraph2, Lines7-9, "These documents, alongside the original query, are then used as context in the LLM to produce a response", wherein Wu explicitly discloses that the system retrieves or receives the LLM-based conversational queries or the multi-round conversation (the corresponding conversation message) that the user's intention is processed to get key words, which then are embedded to perform similarity search. Wu also teaches that the original queries are used as context in the LLM indicating these are saved or stored, rendering it functionally equivalent to the claimed invention.)
Lin and Wu are analogous to the claimed invention as they are from the same field of endeavor of information retrieval and similarity search in high-dimensional vector databases, specifically applied to document and patent retrieval systems. Therefore, it would have been obvious to one of the ordinary skills in the art, before the effective filing date, to combine the multimodal patent vector search framework and full-text database querying system of Lin with the Human-in-the-Loop(HITL) iterative feedback mechanism, result labeling, and dynamic data embedding update pipeline of Wu. The motivation is as recited by Wu (Wu, Pg1, Right Column, Opportunity2, Lines1-3 and Lines9-12, "User feedback is a critical resource for improving the accuracy of similarity search results" and "Ideally, we would like to dynamically adapt the similarity search results according to user feedback for a specific query or question domain without updating the entire database") such that the combination allows the system to continuously adapt feature vector embeddings based on user labeling without the computational overhead of retraining the whole model, thereby eliminating false positives in initial query results and dynamically refining the output to precisely capture subtle technical nuances and specific user search intents in the patent domain.
As to dependent Claim 3,
The combination of Lin and Wu teaches, as mentioned above, all the limitations of Claim 2. It teaches about retrieving the conversation message which the keywords are extracted from it using the LLM processing, then storing them for further use.
Lin, however, does not teach the following limitations, but from the same field of endeavor Wu teaches the extraction system for corporate knowledge base according to claim 2, wherein
the search result is labelled by at least one of an automatic manner and a manual manner, and an approximate vocabulary is selected as the label message based on the natural language processing technology (Wu, Pg4, Section4.1, Paragraph3, Lines2-5, " when the initial similarity search results are presented to the user, they provide feedback on these found examples by labeling them as positive or negative"
Pg4, Section4.1, Paragraph4, Lines2-4, 5-7, "A promising approach to generate labels efficiently is the use of label propagation based on similarity search"
"Subsequently, similarity search retrieves similar examples, which are tentatively labeled as belonging to the same class"
Pg4, Right Column, Conversional Information Retrieval, Lines1-5, 6-7, "Large Language Models (LLMs) facilitate a conversational style of information access. For example, in order to retrieve a specific image from a large collection, a user could engage in a multi-round conversation with the LLM"
"The user then provides feedback, refining the description if the retrieved image isn’t what they were looking for"
Pg4, Section4.2, Lines2-4, "Suppose the similarity search retrieves the top 5 data points, ranked in descending order of similarity: x1,x2,x3,x4,x5. User feedback indicates that x1,x4,x5 are positive results, and x2,x3 are negative"
Pg4, Section4.2, Paragraph2, Lines2-5, "There are three ways of incorporating this feedback: (1) adapting the embedding of the query; (2) adapting the embedding of the data; (3) adapting the similarity function sim", wherein Wu teaches that search results are labeled using manual or automatic techniques, and that language descriptions/embeddings are refined via natural language processing. Wu's implementation of LLMs to refine textual user descriptions and dynamically update vector embeddings based on semantic closeness selects and maps approximate textual terms into vector label representations, rendering it functionally equivalent to the claimed invention.)
Lin and Wu are analogous to the claimed invention as they are from the same field of endeavor of information retrieval and similarity search in high-dimensional vector databases, specifically applied to document and patent retrieval systems. Therefore, it would have been obvious to one of the ordinary skills in the art, before the effective filing date, to combine the multimodal patent vector search framework and full-text database querying system of Lin with the Human-in-the-Loop(HITL) iterative feedback mechanism, result labeling, and dynamic data embedding update pipeline of Wu. The motivation is as recited by Wu (Wu, Pg1, Right Column, Opportunity2, Lines1-3 and Lines9-12, "User feedback is a critical resource for improving the accuracy of similarity search results" and "Ideally, we would like to dynamically adapt the similarity search results according to user feedback for a specific query or question domain without updating the entire database") such that the combination allows the system to continuously adapt feature vector embeddings based on user labeling without the computational overhead of retraining the whole model, thereby eliminating false positives in initial query results and dynamically refining the output to precisely capture subtle technical nuances and specific user search intents in the patent domain.
As to dependent Claim 4,
The combination of Lin and Wu teaches, as mentioned above, all the limitations of Claim 1. It teaches the overall architecture of retrieval-augmented generation(RAG) which the main server communicates with the terminal device to receive user's request to a search of a patent, which the search texts are then vectorized to find similar vectors from the database, which then the user adds labels to the result, which are then vectorized to augment or update the database, which then retrieves the search result based on the updated database.
Lin further teaches the extraction system for a corporate knowledge base according to claim 1, wherein
one of the pieces of the patent raw data comprises a case status, and wherein when the search result is re-integrated, the case status corresponding to the received piece of patent raw data is embedded in the search result (Lin, Pg12, Step101, Paragraph4, Lines1-3, "Among them, the patent filter conditions include but are not limited to any one or more of the following: applicant, inventor, address, classification number, patent agency, brief description and patent status"
Pg14, Step104, Paragraph2, Lines1-4, "Specifically, determine the patent information corresponding to several patent identifiers from the full-text search database according to the patent identifiers corresponding to each of the similarity vectors; determine multiple target patents from the patent information according to the patent filter conditions"
Pg14, Step105, Lines1-2, "the target patent information may be sent to the terminal device, and after receiving the target patent information, the terminal device may display it", wherein Lin explicitly discloses that patent search and filtering operate on metadata attributes, including the patent status as a core metadata attribute associated with each patent record in the database. Lin also teaches operation of querying the full-text database for patent information matching the patent status and outputting the resulting, which this status is included (the corresponding embedding), rendering it functionally equivalent to the claimed invention.)
As to independent Claim 6,
it is a method claim that contains similar limitations of Claim 1 and thus rejected under the same rationale.
As to dependent Claim 7,
it is a method claim that contains similar limitations of Claim 2 and thus rejected under the same rationale.
As to dependent Claim 8,
it is a method claim that contains similar limitations of Claim 3 and thus rejected under the same rationale.
As to dependent Claim 9,
it is a method claim that contains similar limitations of Claim 4 and thus rejected under the same rationale.
Claims 5, 10 are rejected under 35 U.S.C. 103 as being unpatentable over Lin and Wu as mentioned in Claim1 in further view of Hong-Shin et al. (Hong-Shin), Japanese Patent Application No. JP-2018133077-A, Published in August 2018.
As to dependent Claim 5,
The combination of Lin and Wu teaches, as mentioned above, all the limitations of Claim 1. It teaches the overall architecture of retrieval-augmented generation(RAG) which the main server communicates with the terminal device to receive user's request to a search of a patent, which the search texts are then vectorized to find similar vectors from the database, which then the user adds labels to the result, which are then vectorized to augment or update the database, which then retrieves the search result based on the updated database.
Lin and Wu, however, do not teach the following limitations, but from the same field of endeavor Hong-Shin teaches the extraction system for a corporate knowledge base according to claim 1, wherein
one of the pieces of patent raw data comprises an inventor message, and wherein when the search result is re-integrated, the server-end device selects the piece of patent raw data having the same inventor message as the piece of patent raw data received from the company knowledge base, to build an association recommendation, and embeds the association recommendation into the re-integrated search result (Hong-Shin, Pg6, Paragraph19, Lines1-5, "The bibliographic data attribute can be further classified into a technology classification number attribute (IPC, UPC, etc.), patent holder attribute, inventor attribute, and the like. Therefore, the bibliographic data attribute keyword input field L7 can be further classified into a technical classification number attribute keyword input field L71, a patent holder attribute keyword input field L72 and an inventor attribute keyword input field L73"
Pg3, Paragraph5, Lines12-16, "And a keyword advice provision step of providing the deductive component definition and / or the deductive explanation definition to be included in the search keyword, and recommending a patent search keyword that provides data associated with the search keyword as an advice keyword based on the search keyword"
Pg4, Paragraph10, Lines2-4, "Further comprising a keyword advice selection step of providing a keyword selection interface for allowing a user to select keyword advice provided by the keyword advice provision step"
Pg5, Paragraph11, Lines2-4, "After the keyword advice provided by the keyword advice providing process is selected, a change in the number and / or the number of the plurality of search result patents is displayed", wherein Hong-Shin's disclosure of identifying an inventor attribute within patent bibliographic data, statistically aggregating patent records sharing the same inventor attribute to construct ranked advice keywords, and displaying these advice keywords directly within the search result interface to re-integrated and output the updated search results renders functional equivalence to the claimed invention.)
Lin, Wu and Hong-Shin are analogous to the claimed invention as they are from the same field of endeavor of information retrieval and database search systems, specifically vector-based similarity search and metadata filtering within patent database. Therefore, it would have been obvious to one of the ordinary skills in the art, before the effective filing date, to combine the vector-based patent searching and metadata filtering of Lin, the hybrid similarity search with iterative user filtering mechanisms of Wu with the statistical bibliographic attribute extraction and advice keyword recommendation system of Hong-Shin. The motivation is as recited by Hong-Shin (Hong-Shin, Pg5, Paragraph12, Lines7-12, "In the method of recommending the search keyword of the present invention, the search keyword and the data relevance advise keyword related to the patent practice are obtained, and hence the present invention is more scientific and logic than the conventional synonym system , So that the precision of the search is high and the search direction is accurate so that it can be induced in response to demand, so that the number of search results can be reduced") such that the combination allows the user to quickly narrow down search scope, eliminate redundant records, and dynamically re-integrate search results with significantly improved precision and efficiency.
As to dependent Claim 10,
it is a method claim that contains similar limitations of Claim 5 and thus rejected under the same rationale.
Conclusion
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/DONG YOON JUNG/Examiner, Art Unit 2145
/CESAR B PAULA/Supervisory Patent Examiner, Art Unit 2145
