DETAILED ACTION
This communication is in response to the Application filed on 11/22/2024. Claims 1-18 are pending and have been examined. Claims 1, 17 and 18 are independent. This Application was published as U.S. Pub. No. 2025/0348680A1.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 11/22/2024 and 01/22/2025 was filed. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Priority
This application is a 371 of PCT/EP2023/06427 submitted on 05/26/2023.
Acknowledgment is made of applicant’s claim for foreign priority based on application FR2205195 filed in FR Institut National de la Propriété Industrielle on 05/31/2022 and receipt of a certified copy thereof.
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 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
Regarding Claims 1, 17 and 18,
Claim 1 recites a method, which falls under the statutory category of process. Claims 17 and 18 recite a non-transitory computer-readable storage medium and an information-processing device, which fall under the statutory category of manufacture and machine, respectively (Step 1: Yes).
Claims 1, 17 and 18 recite limitations “(a) obtaining textual data…” and “(b) issuing a recommendation relating to data…”. Except for the recitation of an information-processing device and at least one currently-running computer application, limitation (b) is a concept, which can be performed in the human mind through observation, evaluation, or judgement, or by a human using a pen and paper. The claims, under their broadest reasonable interpretation, cover the concept of a librarian receiving the reference search request from the student, analyzing the required contents (field of interest, topics, authors, etc.), searching the reference catalog systematically to match the requested contents, and recommending the references that matches the request perfectly or closest one she/he can find (see MPEP 2106.04(a)(2) III.
Under its broadest reasonable interpretation when read in light of the specification, the actions recited in limitation (b) encompasses mental processes practically performed in the human mind. According, the claim recites an abstract idea (Step 2A, Prong one).
The judicial exception is not integrated into a practical application. In particular, limitation (b) recites an additional elements, “at least one currently-running computer application” implemented by an information-processing device , but they are recited at a high level of generality (i.e., software running on a generic computing device performing a generic computer functions such as processing and storing data from given input) such that it amounts to no more than mere instructions to apply to the exception using a generic computer component.
The claim recites the following additional limitation (b). Limitation (a) is recited at a high level of generality, and amounts to mere data gathering, which is a form of insignificant extra-solution activity. Each of the additional limitations is no more than mere instructions to apply the exception using a generic computer component.
Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea and the claim is therefore directed to the judicial exception. (Step 2A: YES).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because they do not include subject matter that could not be performed by a human, as discussed above with respect to integration of the abstract idea into a practical application, the additional element of using the generic computing elements to perform the claimed elements amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
As noted previously, the claim as a whole merely describes how to generally linking the use of the aforementioned concept to a particular technological environment or field of use. Thus, even when viewed as a whole, nothing in the claim adds significantly more (i.e., an inventive concept) to the abstract idea. The claim is not patent eligible. (Step 2B: NO).
Regarding Dependent Claims 2-16,
Claims 2-16 are dependent on supra claim and includes all the limitations of the claim and further limits the elements of Claim 1. Therefore, the dependent claims recite the same abstract idea. The claim recites the additional limitations of “identification of at least part of the required textual data…” in claim 2, “establishing the first semantic graph…” and “performing a search…” in claim 6, “managing a human-machine interface…” and “sending a message…” in claim 7, “supplying the computer application…” in claim 8, “processing said audio data…” and “convert the speech signals…” in claim 13, “implementing character recognition…” in claim 14, which are no more than mere instructions to apply the exception using a generic computer component, generally linking the use of the judicial exception to a particular technological environment or field of use, insignificant extra-solution activity, or that are well understood, routine and conventional activities previously known to the industry.
No additional elements beyond the use of generic computing elements are claimed, therefore the judicial exception is not integrated into a practical application nor are the claim elements sufficient to amount to significantly more than the judicial exception. Therefore, claims are not patent eligible.
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.
Claims 1-3, 5-11 and 13-18 are rejected under 35 U.S.C. 103 as being unpatentable over Galitsky (US Pub 2022/0138432) in view of Fuchs (US Pat 10,078,633).
Regarding Claim 1,
Galitsky discloses a method implemented by an information-processing device (Fig.11, par [142], computing subsystem 1100), comprising:
obtaining textual data based on data delivered by at least one source (paras [005-006], "…The method may comprise receiving a user query comprising the complex question...generating first linguistic data corresponding to the user query... ", "…obtaining, from a corpus of unstructured texts, an answer candidate text corresponding to the user query ..."; Fig.1, par [031], "…Computing device 110 accesses question 171 (e.g., an utterance), analyzes text of the utterance...", "…determines an answer from an answer candidate text (e.g., text 180)..."), and
issuing a recommendation relating to data required by at least one currently-running computer application (par [055], "…The notion of word similarity is very useful in larger semantic tasks. Knowing how similar two words are... is a useful component of natural language tasks like question answering, recommendation, paraphrasing, and summarization..."; i.e., recommendation (e.g., answers) relating to data required (e.g., user query comprising a complex question)) with which at least one user interface of said device allows interaction (Fig. 11, paras [146-148], "…I/O subsystem 1108 may include user interface input devices and user interface output devices…."), based on a semantic similarity between at least some of said obtained data and at least some of said required data (Fig.2, paras [058-061], "…Once an AMR (abstract meaning representation) of each of the question 171 (i.e., required data) and a candidate answer (i.e., obtained data) are generated, a maximum common sub-AMR representation can be computed by the semantic parser 212…"; par [059] describes the process of matching a semantic tree for a question to a semantic tree for an answer).
Galitsky discloses the notion of word similarity is useful in semantic tasks such as question answering or recommendation, but does not explicitly teach "issuing a recommendation." However, Fuchs, in the analogous field of methods for using semantic understanding and conceptual graph techniques in storage, searching, retrieving and providing of data or other content or information, teaches issuing a recommendation relating to data required by at least one currently-running computer application (Fuchs, Figs.11-13, Fig.19, col.14, lls.16-36, "…in step 232, input text, or example in the form of a user query, is received into the system, and its words are parsed for linkages using a link grammar Lexicon (as in Fig.12)...In step 234 the input text is then transformed into a TCG (tuple conceptual graph, Fig.13)...In step 238, if a match is found, then matching TCG and their corresponding (plain language) equivalents can be returned to, e.g. the user in the form of a response to their query, or suggested alternatives, advertised products, recommendations for similar interests, etc.")
Therefore, it would have been obvious to one of ordinary skill in the art, before effective filing date of the claimed invention, to have modified the generation and validation of answers to complex questions using discourse analysis and the neural machine reading comprehension as taught by Galitsky with processing semantic tasks such as suggestions or recommendation using a linearized tuple-based concept graph from user input as taught by Fuchs with a reasonable expectation of success to allow for efficient storage and searching of such data, in a manner that allows for ease of use by the user, and also provides for additional industrial uses (Fuchs, col.1, ll.45 - col.2, ll.9, Fig.19).
Regarding Claim 2,
Galitsky in view of Fuchs discloses the method according to claim 1 comprising an identification of at least part of the required textual data among the obtained textual data (Galitsky, Fig.2, paras [058-061], "…Once an AMR (abstract meaning representation) of each of the question 171 (i.e., required data) and a candidate answer (i.e., obtained data) are generated, a maximum common sub-AMR representation can be computed by the semantic parser 212…"), and wherein said recommendation is based on the identified textual data (Fuchs, Fig.19, col.14, lls.16-36, "...In step 238, if a match is found, then matching TCG and their corresponding (plain language) equivalents can be returned to, e.g. the user in the form of a response to their query, or suggested alternatives, advertised products, recommendations for similar interests, etc.").
Regarding Claim 3,
Galitsky in view of Fuchs discloses the method according to claim 1, wherein said required data are data currently being written by a user of said device (Galitsky, Fig.11, par [146], User interface input devices may include a touchpad or touch screen incorporated into a display, which allow the user to write directly onto the display using fingers or a stylus).
Regarding Claim 5,
Galitsky in view of Fuchs discloses the method according to claim 1, wherein said semantic similarity takes into account a similarity between a first semantic graph established for said obtained data and a second semantic graph established for said required data (Galitsky, par [006], "…the method may comprise generating a syntax tree from the user query (i.e., required data) ( or the answer candidate text (i.e., obtained data))… generating an abstract meaning representation of the user query ( or the answer candidate text)… generating a discourse tree from the user query ( or the answer candidate text)..."; see paras [053-061] for matching similarity between semantic trees for questions and answers).
Regarding Claim 6,
Galitsky in view of Fuchs discloses the method according to claim 1, comprising:
establishing the first semantic graph based at least on textual data to be verified (Fuchs, Fig.18, col.13, lls.8-56, "…step 226, a first TCG is either created, based on an input text...step 227, a second TCG is again either created based on retrieved from a previously stored TCG..."),
performing a search for an at least partial similarity between the first and second semantic graphs in order to identify at least part of the textual data to be verified among the obtained textual data (Fuchs, Fig.18, step 228: TCG names or other TCG identifiers are compared to quickly determine an exact match. Step 229: if TCG names are different, then compare the TCG relationships and variables in each TCG with one another to determine partial matches.), and
issuing a correction recommendation if in response to data from the identified part differing from the corresponding obtained textual data (Fuchs, Figs.18-19, …step 230: Results of the TCG match are output and/or used for some subsequent purpose. Step 238: matching results are returned to the user in the form of a response to their query, or depending on the particular implementation, suggested alternatives, advertised products, recommendations..." ).
Regarding Claim 7,
Galitsky in view of Fuchs discloses the method according to claim 1, comprising:
managing a human-machine interface that is connected to the information processing device, and sending a message via the human-machine interface, corresponding to said recommendation (Galitsky, Fig.11, paras [146-148], I/O subsystem 1108 may include user interface input devices and user interface output devices...").
Regarding Claim 8,
Galitsky in view of Fuchs discloses the method according to claim 7, wherein the human- machine interface comprises a screen displaying at least a first window specific to the computer application, and the method comprises: controlling a displaying of said recommendation in a second window on the screen, simultaneously with the first window (Galitsky, Fig.11, par [148], "…User interface output devices may include a display subsystem...use of the term "output device" is intended to include all possible types of devices and mechanisms for outputting information from computing subsystem 1100 to a user..."; i.e., features disclosed in the claim are well-known routine for a person skilled in the art implementing human-machine interfaces.) .
Regarding Claim 9,
Galitsky in view of Fuchs discloses the method according to claim 1, wherein the recommendation comprises the identified textual data, and the method comprises: supplying the computer application with the identified textual data (Galitsky, paras [098-099], "At 808, the first linguistic data and the second linguistic data are provided to a machine-learning model...At 810, the answer identified from the answer candidate text is received from the machine-learning model…."; Fuchs, Figs.18-19, …step 230: Results of the TCG match are output and/or used for some subsequent purpose. Step 238: matching results are returned to the user in the form of a response to their query, or depending on the particular implementation, suggested alternatives, advertised products, recommendations...").
Regarding Claim 10,
Galitsky in view of Fuchs discloses the method according to claim 1, wherein the first and second semantic graphs are tree structures (Galitsky, Fig.3, par [053], "…semantic parser 212 can generate a semantic tree. A semantic tree (or graph) includes nodes and edges. The nodes represent entities (e.g., a place, person, or thing). Each edge represents a relationship between two of the entities...") and comprise: nodes representing predicates, and leaves representing textual data and being responses to the predicates, the method comprising:
identifying nodes with common predicates between the first and second graphs, and in the second graph, selecting leaves coming from the nodes with common predicates, said selected leaves corresponding to said part of the required textual data among the obtained textual data (Galitsky, paras [056-061], "…matching a semantic tree ( e.g., an unflattened version of an AMR representation of question 171) for a question to a semantic tree for an answer ( e.g., an unflattened version of an AMR representation of the portion of text 180)…(1) locating the topic entity in the question, (2) finding the main relationship between the answer and the topic entity, and (3) expanding the query graph with additional constraints that describe properties the answer needs to have, or relationships between the answer and other entities in the question…").
Regarding Claim 11,
Galitsky in view of Fuchs discloses the method according to claim 1, wherein said first and second semantic graphs are in abstract semantic representation computer language, or Abstract Meaning Representation (AMR) computer language (Galitsky, par [054], "…the semantic parser 212 may be configured to generate an abstract meaning representation (AMR) of input text (e.g., the question 171, the text 180, etc.). AMR is a semantic representation language that models input text as a graph that is a rooted, labeled, directed, acyclic graph (DAG) that comprises the entire text...").
Regarding Claim 13,
Galitsky in view of Fuchs discloses the method according to claim 1, wherein said data delivered by at least one source are audio data, and the method comprises: processing said audio data in order to detect speech signals and convert the speech signals into textual data (Galitsky, par [146], "…audio input devices with voice command recognition systems, microphones...").
Regarding Claim 14,
Galitsky in view of Fuchs discloses the method according to claim 1, wherein said data delivered by at least one source are data delivered by a computerized device for capturing handwritten characters, and the method comprises: implementing character recognition for handwritten characters in order to convert the entered characters into textual data (Galitsky, Fig.11, par [146], User interface input devices may include a touchpad or touch screen incorporated into a display and it is construed that touchpad or screen would allow the user to write directly onto the display using fingers or a stylus and the handwritten characters are converted into textual data.).
Regarding Claim 15,
Galitsky in view of Fuchs discloses the method according to claim 1, wherein said data delivered by at least one source are stored in a memory of the information-processing device for a first period of time following a latest use in implementing the method (Galitsky, par [150], "…system memory 1110 may be volatile (such as random access memory (RAM)) and/or nonvolatile (such as read-only memory (ROM), flash memory, etc.) The RAM typically contains data and/or program modules that are immediately accessible to and/or presently being operated and executed by processing unit 1104...").
Regarding Claim 16,
Galitsky in view of Fuchs discloses the method according to claim 2, comprising implementing artificial intelligence programmed to learn relevant recommendations on the basis of user feedback (Galitsky, par [030], "…Computing device 110 can include one or more of autonomous agent 112, deep learning module 118, and knowledge database 120..."; par [031], "…computing device 110 interacts with user device 160 in a dialogue session (i.e., user feedback)..."; Fig.2, deep learning module 200), and,
after learning, to select a relevant recommendation to be issued on the basis on the identified textual data (Fuchs, Fig.19, col.14, lls.16-36, "...In step 238, if a match is found, then matching TCG and their corresponding (plain language) equivalents can be returned to, e.g. the user in the form of a response to their query, or suggested alternatives, advertised products, recommendations for similar interests, etc.").
Claim 17 is a non-transitory computer-readable storage medium claim with limitations similar to the limitations of Claim 1 and is rejected under similar rationale. Additionally, Galitsky discloses a non-transitory computer-readable storage medium storing a computer program comprising instructions which, when executed by a processor, cause the processor to perform a method (Fig.11, paras [151-154], "…Storage subsystem 1118 may also provide a tangible computer-readable storage medium for storing the basic programming and data constructs that provide the functionality...This can include tangible, non-transitory computer-readable storage media...") comprising
…
Rationale for combination is similar to that provided for Claim 1.
Claim 18 is an information-processing device claim with limitations similar to the limitations of Claim 17 and is rejected under similar rationale. Additionally,
Galitsky discloses at least one processor (Fig.11, par [144], Processing unit 1104); and at least one non-transitory computer readable medium storing a computer program comprising instructions which, when executed by the at least one processor, cause the information-processing device to perform a method (Fig.11, paras [151-154], "…Storage subsystem 1118 may also provide a tangible computer-readable storage medium for storing the basic programming and data constructs that provide the functionality...This can include tangible, non-transitory computer-readable storage media...") comprising:
...
Rationale for combination is similar to that provided for Claim 1.
Claims 4 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Galitsky in view of Fuchs in view of Liu et al. (US Pat 10,635,748).
Regarding Claim 4,
Galitsky in view of Fuchs discloses the method according to claim 1, wherein said recommendation comprises a proposal of elements that correct said required data (Galitsky, par [022], "…These techniques can be used to validate and/or correct answers generated by deep learning systems..."; Fuchs, Fig.19, col.14, lls.16-36, "...in the form of a response to their query, or suggested alternatives, advertised products, recommendations for similar interests, etc."), but does not explicitly teaches "said recommendation comprises a proposal of elements that complete said required data.
Liu, in the field of explicitly discloses a proposal of elements that complete said required data (Liu, Fig.4, col.9, ll.7 - col.10, ll.33, "…The group of data sources 401-405 are consumed for a cognitive auto-fill content recommendation system 430 such as, for example, cognitive auto-fill content recommendation system 430 using natural language processing (NLP) and artificial intelligence (AI) to provide processed content..."; col.10, lls.20-27, "…As the NLP system 410 (including the machine learning component 438) learns different sets of data, a characteristics association component 412 (or "cognitive characteristics association component") may use the artificial intelligence to make cognitive associations or links between data sources 401-405 by determining common concepts, methods, features, similar characteristics, and/or an underlying common topic…").
Therefore, it would have been obvious to one of ordinary skill in the art, before effective filing date of the claimed invention, to have modified the neural machine reading comprehension (MRC) model in data search, retrieval, and storage as taught by Galitsky in view of Fuchs with cognitive auto-fill content recommendation of Liu with a reasonable expectation of success to allow for the sharing of information between users in an increasingly user friendly and simple manner (Liu, col.1, lls.16-27).
Regarding Claim 12,
Galitsky in view of Fuchs discloses the method according to claim 1, wherein said data delivered by at least one source are image data containing text (Galitsky, par [032], "…Examples of suitable text include electronic text source such as text files, Portable Document Format (PDF)® documents..."; par [147], image scanners), but neither Galitsky nor Fuchs explicitly teaches "processing said image data by character recognition in order to obtain textual data."
Liu teaches processing said image data by character recognition in order to obtain textual data (Fig.6, col.15, lls.3-13, "…block 618, format convertor( s) may be a set of components ( e.g., servers) that may be used for converting a detected communication message (e.g., an image, audio, video, etc.) in a source application to a selected format ( e.g., a text format)...an optical character recognition ("OCR") operation may convert image input to text data, as in block 622...").
Rationale for combination is similar to that provided for Claim 4.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Dobson et al. (US Pat 11,288,083) discloses a computer system providing recommended actions based upon text extracted across a plurality of shared applications by one or more client devices. the server computer system also illustratively includes a relevance engine which cooperates with the extractor to generate a concept map associating the extracted text with actions initiated by the client computing devices after displaying respective text on the displays (Dobson, col.6:20 - col.7:67).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JANGWOEN LEE whose telephone number is (703)756-5597. The examiner can normally be reached Monday-Friday 8:00 am - 5:00 pm ET.
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/JANGWOEN LEE/Examiner, Art Unit 2656
/BHAVESH M MEHTA/Supervisory Patent Examiner, Art Unit 2656
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