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 .
DETAILED ACTION
This action is in response to application filed 12/22/2023.
Claims 1-23 are pending in this application.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 04/28/2025 has been placed in record and considered by the examiner.
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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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, 7, 10, 12-17, 19-23 are rejected under 35 U.S.C. 103 as being unpatentable over Wu (CN 104915413 B) in view of Bui et al. (US 2024/0339217 A1 – Priority Date 04/07/2023)
Regarding claim 1, Wu discloses a method, performed by a computer system in a network, the method comprising:
receiving, by means of one or more network crawler modules of the computer system, at least one input message indicative of at least one subject-related keyword (pg. 4, [0015]: The search module 12 sends a search request to the server 2 according to the input keyword, and after obtaining the response of the server 2, displays the disease information searched by the server 2 to the user. The search module 12 (e.g. crawler) provides the user with the function of querying the disease by using the keyword, and can understand the disease more specifically);
in response to receiving the input message, obtaining, via the network, subject- related information from one or more data repositories, which are hosted by one or more network nodes and contain a plurality of data objects, wherein each of the plurality of data objects is adapted to be rendered by a network browser (pg. 6, [0011]: query instruction to the database 3 according to the browsing request, and send the data returned by the database 3 (e.g. repository node) to the mobile terminal according to a predetermined format) and wherein obtaining the subject-related information comprises:
accessing, via the network, at least one prioritized subject-related web service, the at least one prioritized subject-related web service including an integrated search functionality, providing the at least one subject-related keyword to the integrated search functionality of the at least one prioritized subject-related web service (pg. 2, [0019]: The web crawler module uses a web crawler to automatically search for disease-related web pages on the Internet (e.g. search functionality), download web pages, analyze valuable data in web pages, and store them in a database. pg. 7, [0014]: sorting the ranking results, and sending result information (e.g. prioritized web service) to the terminal, including: disease list, disease information, and department information. Pg. 9, [0008]: searching for the disease name, obtaining a return page, and parsing the link in the page,
receiving, in response to providing the at least one subject-related keyword, a search results page from the at least one prioritized subject-related web service, the search results page including at least one identifier of at least one data object contained in the one or more data repositories pg. 7, [0014]: sorting the ranking results, and sending result information (e.g. prioritized web service) to the terminal, including: disease list, disease information, and department information. Pg. 9, [0008]: searching for the disease name, obtaining a return page, and parsing the link (e.g. identifier) in the page),
crawling, by means of the one or more network crawler modules, the one or more data repositories based on the at least one data object associated with the at least one identifier, wherein the crawling includes rendering, by means of at least one browser engine of the computer system, any data object subject to the crawling (pg. 9, [0009]: obtaining a link page… determining whether the structured information of the disease exists in the linked page, determining whether the structured information has been stored in the database, and if not, parsing the structured data from the webpage and storing the structured data in the database).
However, Wu does not disclose analyzing, by means of the computer system and using a first machine learning model, a rendered content of each rendered data object to determine subject-related information encoded in each data object, respectively, processing the subject-related information to determine at least one classification indicator associated with the subject-related information, the classification indicator indicative of a classification of the subject-related information with respect to the at least one subject-related keyword using a second machine learning model, and storing the processed subject-related information and the at least one associated classification indicator in a system database, wherein subject-related information stored in the system database is accessible for generating, by means of the computer system and using a third machine learning model, an output message for output towards one or more user terminals.
In an analogous art, Bui discloses analyzing, by means of the computer system and using a first machine learning model, a rendered content of each rendered data object to determine subject-related information encoded in each data object ([0060]: The search query 12 can be processed with an intent classification model 14 to generate an intent classification 18 associated with the search query 12. The intent classification 14 can be descriptive of a determined intent of the search query 12. For example, the intent classification 18 (e.g. first machine learning model) for the search query 12 may be descriptive of a diagnostic intent based on determining the image depicts an abnormality (e.g., a skin abnormality (e.g., a lesion on the skin). The intent classification model 14 can include one or more machine-learned models and may include one or more classifier heads. The intent classification 16 can be based on semantic understanding, determined objects in the image, determined medical abnormalities in the image, and/or context data), respectively, processing the subject-related information to determine at least one classification indicator associated with the subject-related information, the classification indicator indicative of a classification of the subject-related information with respect to the at least one subject-related keyword using a second machine learning model, and storing the processed subject-related information and the at least one associated classification indicator in a system database ([0084]: the search query may include a multimodal query. For example, the search query may include the one or more images and a text query associated with the one or more images (e.g., one or more images of a rash and a text query stating “why do I have this rash?”). [0061]: the image of the search query 12 may then be processed with a condition classification model 18 (e.g. second machine learning model) to generate and/or determine a condition classification 20. The condition classification model 18 can include one or more machine-learned models and may be trained on ground truth images labeled by medical professionals. The condition classification model 18 may include one or more detection models, one or more segmentation models, one or more augmentation models, and/or one or more condition-specific classification models. [0063]: Condition information 24 can then be obtained from a database 22 based on the condition classification 20. The condition information 24 can include a medical condition name(s), example medical image(s) for the respective medical condition, and/or description(s) associated with the respective medical condition. The database 22 can include a curated database that includes data verified by medical professionals and may be queried and/or searched based on the condition classification 20), wherein subject-related information stored in the system database is accessible for generating, by means of the computer system and using a third machine learning model, an output message for output towards one or more user terminals ([0100]: the user may be provided with search suggestions based on the output of a transformer model (e.g. third machine learning model) that may be trained on image and text data to provide suggestions for providing a more accurate preliminary diagnosis and/or search).
Therefore, it would have been obvious before the effective filed date of the claimed invention to a person having ordinary skill in the art to modify Wu to comprise “analyzing, by means of the computer system and using a first machine learning model, a rendered content of each rendered data object to determine subject-related information encoded in each data object, respectively, processing the subject-related information to determine at least one classification indicator associated with the subject-related information, the classification indicator indicative of a classification of the subject-related information with respect to the at least one subject-related keyword using a second machine learning model, and storing the processed subject-related information and the at least one associated classification indicator in a system database, wherein subject-related information stored in the system database is accessible for generating, by means of the computer system and using a third machine learning model, an output message for output towards one or more user terminals” taught by Bui.
One of ordinary skilled in the art would have been motivated because it would have enabled to utilizing a plurality of classification models to determine a search query and to determine candidate medical conditions (Bui, [0002]).
Regarding claim 2, Wu-Bui discloses the method of claim 1, wherein: the at least one input message is received from a user terminal via the network, and/or the at least one keyword has been generated by means of a keyword generator module of the computer system, and the at least one input message is received from the keyword generator module (Wu, pg. 4, [0016]-[0017]: The browsing module 11 is configured to send a browsing request to the server 2, and after obtaining the response of the server 2, display the obtained data to the user. The search module 12 sends a search request to the server 2 according to the input keyword, and after obtaining the response of the server 2, displays the disease information searched by the server 2 to the user).
Regarding claim 3, Wu-Bui discloses the method of claim 1, wherein the at least one subject-related keyword, the subject-related information and the at least one subject-related prioritized web service are related to a same subject, wherein the same subject includes a health-related subject (Wu, pg. 1, [0008]: Receiving, by the server, the health report request, calculating, according to the keyword group of the health report request, the most relevant disease of the keyword group by using a naive Bayes algorithm, and requesting information about the most relevant disease from the database).
Regarding claim 4, Wu-Bui discloses the method of claim 1, wherein the classification indicator is indicative of a determined relevance of the subject-related information to the at least one subject-related keyword, the relevance determined using the second machine learning model (Bui, [0055]: The systems and methods may include leveraging the predicted condition classifications to adjust the search query (e.g., augment the search query to include text data descriptive of the predicted condition classifications), adjust visual search result rankings (e.g., boost the rankings of visual search results associated with the one or more predicted condition classifications). The same rationale applies as in claim 1.
Regarding claim 7, Wu-Bui discloses the method of claim 1, further comprising training, by means of the computer system, at least one of the first machine learning model, the second machine learning model or the third machine learning model using the subject-related information and associated classification indicators stored in the system database (Bui, [0106]: The object classifier 604, the intent classifier 606, and the diagnosis classifier 608 may be machine-learned models trained on labeled training datasets). The same rationale applies as in claim 1.
Regarding claim 10, Wu-Bui discloses the method of claim 1, wherein crawling the one or more data repositories based on the at least one data object associated with the at least one identifier comprises crawling the one or more data repositories starting from the at least one data object associated with the at least one identifier and continuing according to at least one URL determined based on the at least one data object, the at least one URL associated with at least one other data object (Wu, pg. 5, [0004]-[0005]: The browsing module 11 of the mobile terminal 1 is mainly responsible for transmitting browsing request information to the server 2, requesting parameters by means of a URL, and accepting a response from the server 2. After receiving the information transmitted from the server 2, the mobile terminal 1 formats it and then outputs it to the user visual interface; wherein the disease list sends the request to the server 2 in the selected category).
Regarding claim 12, Wu-Bui discloses the method of claim 1, wherein the network comprises the Internet (Wu, pg. 5, [0008]: The specific implementation of the search module 12 is similar to that of the browsing module 11, by transmitting a search request to the server 2 using the Internet, receiving response data from the server, formatting it, and outputting it to the user's visual interface).
Regarding claim 13, Wu-Bui discloses the method of claim 1, wherein the plurality of network nodes comprises one or more servers, and the at least one data repository comprises a repository of web-sites, wherein the at least one data object is comprised by a website stored by means of the one or more servers (Wu, pg. 8, [0001]: The web crawler module 31 uses a web crawler to automatically search for a disease-related web page on the Internet, download the web page, analyze valuable data in the web page, and store the data in the database.).
Regarding claim 14, Wu-Bui discloses the method of claim 1, wherein the plurality of network nodes further comprises at least one clinical database which stores one or more clinical data objects (Bui, [0007]: the operations can include obtaining the medical condition information associated with the one or more candidate medical conditions from a curated medical information database. The medical condition information can include a medical condition name and one or more condition images), wherein the method further comprises retrieving at least one of the one or more clinical data objects from the at least one clinical database and storing, as subject-related information, health-related information determined based on the at least one clinical data object in the system database (Bui, [0092]: obtain the medical condition information associated with the one or more candidate medical conditions from a curated medical information database. The medical condition information can include a medical condition name and/or one or more condition images. The one or more condition images can depict an example of the respective candidate medical condition. In some implementations, the one or more condition images can be obtained from a medical condition image database. The medical condition image database can include a plurality of medical condition images selected by one or more medical professional). The same rationale applies as in claim 1.
Regarding claim 15, Wu-Bui discloses the method of claim 1, further comprising: receiving from at least one user terminal user monitoring data indicative of at least one health-related condition of a user detected by means of the user terminal (Bui, [0049]: The user can capture (e.g., using a camera of a user computing device) a plurality of images of the identified area of the patient's skin using a computing device such as a smartphone or digital camera. The captured images can be provided to a machine-learned skin condition classification model which is located locally and/or remotely. The machine-learned skin condition classification model can generate a skin condition classification for the identified portion of the patient's skin), and storing, as subject-related information, health-related information determined based on the user monitoring data in the system database (Bui, [0063]: Condition information 24 can then be obtained from a database 22 based on the condition classification 20. The condition information 24 can include a medical condition name(s), example medical image(s) for the respective medical condition, and/or description(s) associated with the respective medical condition). The same rationale applies as in claim 1.
Regarding claim 16, Wu-Bui discloses the method of claim 1, wherein the keyword is included in a health-related query contained in the input message and wherein the method further comprises generating a response message based on the health-related information stored as subject- related information in the system database using the third machine learning model for output towards the user terminal (Bui, [0100]: the user may be provided with search suggestions based on the output of a transformer model (e.g. third machine learning model) that may be trained on image and text data to provide suggestions for providing a more accurate preliminary diagnosis and/or search). The same rationale applies as in claim 1.
Regarding claim 17, Wu-Bui discloses the method of claim 16, wherein the health-related query relates to an individual condition of a user of the user terminal or of another person and the response message is indicative of a personalized treatment suggestion for the user or the other person (Bui, [0100], the user may be provided with search suggestions based on the output of a transformer model that may be trained on image and text data to provide suggestions for providing a more accurate preliminary diagnosis and/or search). The same rationale applies as in claim 1.
Regarding claim 19, Wu-Bui discloses the method of claim 1, wherein the input message comprises voice input data, and wherein the method further comprises processing the voice input data using a fourth machine learning model to determine a keyword based on the voice input data (Bui, [0059]: e search query may additionally include text data (e.g., a text query descriptive of a question, symptoms, and/or other details), additional image data, user profile data (e.g., search history data, user browsing history data, and/or other profile information), audio data (e.g., a voice command), and/or latent encoding data). The same rationale applies as in claim 1.
Regarding claim 20, Wu-Bui discloses the method of claim 4, wherein training any of the machine learning models is performed at least partially with respect to one or more of the following criteria: one or more underlying causes of a disease, one or more mechanisms of actions of one or more medicines, an increased potency of one or more medicines, a precision dose of one or more treatments to reduce side effects (Bui, [0087]: the medical conditions classification model may be trained and/or configured to classify conditions in a plurality of different medical fields. [0106]: The object classifier 604, the intent classifier 606, and the diagnosis classifier 608 may be machine-learned models trained on labeled training datasets). The same rationale applies as in claim 1.
Regarding claim 21, Wu-Bui discloses the method of claim 1, wherein the computer system comprises a distributed computing architecture and wherein at least some of the functionalities of the computer system are implemented at different instances of the distributed computing architecture (Wu, pg. 3, [0007]-[0008]: The mobile terminal is used for user access to the system, and sends a browsing request, a search request, or a health report request to the server. Server, configured to process command and system data between the user and the system, and after receiving the request sent by the mobile terminal, send a query instruction, a search instruction, or a health report instruction to the database; and send the data returned by the database to the mobile according to a specified format).
Regarding claims 22 and 23; the claims are interpreted and rejected for the same reason as set forth in claim 1.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable Wu in view of Bui, as applies to claim 1, in further view of Pistoia et al. (US 2016/0210361 A1).
Regarding claim 5, Wu-Bui discloses the method of claim 1.
However, Wu-Bui does not disclose wherein the crawling is performed until a fulfilment of a stop condition, in particular an expiry of a pre-set crawling period associated with the input message, is determined.
In an analogous art, Pistoia discloses wherein the crawling is performed until a fulfilment of a stop condition, in particular an expiry of a pre-set crawling period associated with the input message, is determined ([0057]: the computing system 100 determines whether the website has been crawled. If not (block 309=No), the flow continues at block 290. If so (block 309=Yes), the flow continues at block 310. Note that other criteria, such as a time period, may be used to stop crawling of a website).
Therefore, it would have been obvious before the effective filed date of the claimed invention to a person having ordinary skill in the art to modify Wu-Bui to comprise “wherein the crawling is performed until a fulfilment of a stop condition, in particular an expiry of a pre-set crawling period associated with the input message, is determined” taught by Pistoia.
One of ordinary skilled in the art would have been motivated because it would have enabled to pre-set a time period stopping website crawling (Pistoia, [0057]).
Claims 6 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Wu in view of Bui, as applies to claim 1, in view of Jiang et al. (US 2006/0277175 A1).
Regarding claim 6, Wu-Bui discloses the method of claim 1.
However, Wu-Bui does not disclose wherein accessing the at least one prioritized subject-related web service includes accessing, in particular successively, a plurality of prioritized subject-related web services, each of the prioritized subject-related web services including an integrated search functionality, and providing the at least one subject-related keyword comprises providing the at least one subject-related keyword to the integrated search functionality of each of the prioritized subject-related web services.
In an analogous art, Jiang discloses wherein accessing the at least one prioritized subject-related web service includes accessing, in particular successively, a plurality of prioritized subject-related web services, each of the prioritized subject-related web services including an integrated search functionality, and providing the at least one subject-related keyword comprises providing the at least one subject-related keyword to the integrated search functionality of each of the prioritized subject-related web services ([0025]: Queries used by focused crawlers include keywords and keyword examples as well as a set of example pages of the type that are being sought via the crawl together with a classification method for pages. [0130]: construct a meta-search engine, where results from multiple search engines and databases are integrated and presented in a unified form. These results could be used directly without any further processing or used to initiate a focused crawl. What is usually a difficult problem for meta-search engines is the task of merging the results and providing a coherent ranking to the pages from different search engines. Our linkage-graph analysis of reverse crawls allows us to compute uniform importance scores for pages. We can simply get the returns from multiple search engines, crawl forwards and backwards for a few steps from the results, and compute hub and authority scores for all of the pages. We can then simply rank pages based on their authority scores, and can also identify a set of hubs, which are uniformly good and can be used as starting points for the crawl).
Therefore, it would have been obvious before the effective filed date of the claimed invention to a person having ordinary skill in the art to modify Wu-Bui to comprise “wherein accessing the at least one prioritized subject-related web service includes accessing, in particular successively, a plurality of prioritized subject-related web services, each of the prioritized subject-related web services including an integrated search functionality, and providing the at least one subject-related keyword comprises providing the at least one subject-related keyword to the integrated search functionality of each of the prioritized subject-related web services” taught by Jiang.
One of ordinary skilled in the art would have been motivated because it would have enabled to enable the crawler to obtain the most desirable pages as quickly as possible (Jiang, [0004]).
Regarding claim 11, Wu-Bui discloses the method of claim 1.
However, Wu-Bui does not disclose wherein receiving the at least one input message includes receiving a plurality of input messages from one or more user terminals , and wherein the method further comprises queuing the plurality of input messages for processing the plurality of input messages successively.
In an analogous art, Jiang discloses wherein receiving the at least one input message includes receiving a plurality of input messages from one or more user terminals, and wherein the method further comprises queuing the plurality of input messages for processing the plurality of input messages successively ([0025]: URL-queue as a set of queues, each holding pages with different levels of similarity to the query (for example, similarities between different thresholds). Pages to crawl may be chosen from higher-similarity queues before choosing pages from lower-similarity queues (using thresholds and PageRanks as well to avoid too strong biases).
Therefore, it would have been obvious before the effective filed date of the claimed invention to a person having ordinary skill in the art to modify Wu-Bui to comprise “wherein receiving the at least one input message includes receiving a plurality of input messages from one or more user terminals, and wherein the method further comprises queuing the plurality of input messages for processing the plurality of input messages successively” taught by Jiang.
One of ordinary skilled in the art would have been motivated because it would have enabled to enable the crawler to obtain the most desirable pages as quickly as possible (Jiang, [0004]).
Claims 8 are rejected under 35 U.S.C. 103 as being unpatentable over Wu in view of Bui, as applies to claim 1, in view of Kumar et al. (US 2022/0130163 A1).
Regarding claim 8, Wu-Bui discloses the method of claim 1.
However, Wu-Bui does not disclose wherein the rendered content of at least one rendered data object includes non-textual content and wherein analyzing the rendered content includes generating a textual representation of at least parts of the non-textual content and determining subject-related information encoded in the textual representation.
In an analogous art, Kumar discloses wherein the rendered content of at least one rendered data object includes non-textual content and wherein analyzing the rendered content includes generating a textual representation of at least parts of the non-textual content and determining subject-related information encoded in the textual representation ([0040]-[0042], [0047]: At step 302, an input document may be scanned by a scanning module 104, wherein the input document may be converted to a digital image. At step 304, the OCR module 112 may be configured to extract text from the digital image of the input document. At step 306, the extracted text from the digital image of the input document is received by the encoder 204, wherein the extracted text is encoded to a numerical format from the textual form).
Therefore, it would have been obvious before the effective filed date of the claimed invention to a person having ordinary skill in the art to modify Wu-Bui to comprise “wherein the rendered content of at least one rendered data object includes non-textual content and wherein analyzing the rendered content includes generating a textual representation of at least parts of the non-textual content and determining subject-related information encoded in the textual representation” taught by Kumar.
One of ordinary skilled in the art would have been motivated because it would have enabled when a document is scanned, the text segments are extracted from the document using OCR module, wherein the transformer-based language model may be configured to create feature vector representations for each of the extracted words (Kumar, [0047]).
Claims 9 are rejected under 35 U.S.C. 103 as being unpatentable over Wu in view of Bui, as applies to claim 1, in view of Bailey et al. (US 2022/0365998 A1).
Regarding claim 9, Wu-Bui discloses the method of claim 1.
However, Wu-Bui does not disclose wherein the at least one prioritized subject-related web service contains one or more web services provided by a governmental health organization.
In an analogous art, Bailey discloses wherein the at least one prioritized subject-related web service contains one or more web services provided by a governmental health organization ([0053]: crawling a set of websites for target content (operation 204). In some embodiments, web crawler 106 restricts crawling to a set of trusted sites, such as reputable news websites, social media channels managed by verified entities, and government servers. Targeting crawling reduces the processing burden on web crawler 106 while enhancing the reliability of extracted content).
Therefore, it would have been obvious before the effective filed date of the claimed invention to a person having ordinary skill in the art to modify Wu-Bui to comprise “wherein the at least one prioritized subject-related web service contains one or more web services provided by a governmental health organization” taught by Bailey.
One of ordinary skilled in the art would have been motivated because it would have enabled for crawling the web to extract and verify entity event information to enhance entity-based functionality in software applications and cloud services (Bailey, [0002]).
Claims 18 are rejected under 35 U.S.C. 103 as being unpatentable over Wu in view of Bui, as applies to claim 16, in view of Sharma et al. (US 2023/0106284 A1).
Regarding claim 18, Wu-Bui discloses the method of claim 16.
However, Wu-Bui does not disclose wherein the health-related query relates to one or more constraints in a medical treatment development and the response message is indicative of a suggested medical treatment scheme, in particular including a suggested pharmaceutical composition.
In an analogous art, Sharma discloses wherein the health-related query relates to one or more constraints in a medical treatment development and the response message is indicative of a suggested medical treatment scheme, in particular including a suggested pharmaceutical composition ([0132]: asset prioritization to filter the first set of potential drug compositions to determine at least one potential drug composition for the target disease. Herein, asset prioritization is used to find a new suggestion for the first set of potential drug compositions, suggest a combination of one or more potential drug compositions to treat the target disease, assess risk profile of the first set of potential drug compositions to compare with other development options).
Therefore, it would have been obvious before the effective filed date of the claimed invention to a person having ordinary skill in the art to modify Wu-Bui to comprise wherein the health-related query relates to one or more constraints in a medical treatment development and the response message is indicative of a suggested medical treatment scheme, in particular including a suggested pharmaceutical composition” taught by Sharma.
One of ordinary skilled in the art would have been motivated because it would have enabled for generating potential drug compositions for a disease target (Sharma, [0002]).
Additional References
The prior art made of record and not relied upon is considered pertinent to applicants disclosure.
Bulu, US 2023/0085697 A1: Method, Apparatus and Computer Program Product for Graph-Based Encoding of Natural Language Data Objects.
Lakshmikanthan et al., US 11,893,358 B1: Intent-Based Query and Response Routing Between Users and Backend Services.
Conclusion
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/J.C.T/Examiner, Art Unit 2446
/MICHAEL A KELLER/Primary Patent Examiner, Art Unit 2446