DETAILED ACTION
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 11/01/2024 was filed in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,100,393. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims are obvious variations of each other.
Regarding Claim 1 (drawn to an apparatus):
Current Application
Claim 1:
An apparatus of generating directed graph using raw data, the apparatus comprising:
at least a processor; and
a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to:
receive raw data describing an entity;
determine a plurality of execution elements from the raw data;
determine a data extrapolation of the plurality of execution elements; and
generate a directed graph as a function of the data extrapolation.
‘393
Claim 1:
An apparatus of generating directed graph using raw data, the apparatus comprising:
at least a processor; and
a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to:
receive raw data from one or more data sources;
determine a plurality of execution elements from the raw data;
determine a data extrapolation of the plurality of execution elements, wherein determining the data extrapolation further comprises:
determining at least an operation datum for the plurality of execution elements; and
generate a directed graph as a function of the data extrapolation, wherein the directed graph comprises an ordered series of the plurality of execution elements connected using the at least an operation datum.
Regarding Claim 11 (drawn to a method):
Current Application
Claim 11:
A method of generating a directed graph using raw data, the method comprising:
receiving, using at least a processor, raw data describing an entity;
determining, using the at least a processor, a plurality of execution elements from the raw data;
determining, using the at least a processor, a data extrapolation of the plurality of execution elements; and
generating, using the at least a processor, a directed graph as a function of the data extrapolation.
‘393
Claim 11:
A method of generating a directed graph using raw data, the method comprising:
receiving, using at least a processor, raw data from one or more data sources;
determining, using the at least a processor, a plurality of execution elements from the raw data;
determining, using the at least a processor, a data extrapolation of the plurality of execution elements, wherein determining the data extrapolation further comprises:
determining at least an operation datum for the plurality of execution elements; and
generating, using the at least a processor, a directed graph as a function of the data extrapolation, wherein the directed graph comprises an ordered series of the plurality of execution elements connected using the at least an operation datum.
It is clear that all the elements of the application claims 1 and 11 are to be found in patent claims 1 and 11, as the application claims 1 and 11 fully encompasses patent claims 1 and 11. The difference between the application claims 1 and 11 and the patent claims 1 and 11 lies in the fact that the patent claims includes more elements and is thus more specific. Thus the invention of claims 1 and 11 of the patent is in effect a “species” of the “generic” invention of the application claims 1 and 11. It has been held that the generic invention is “anticipated” by the “species”. See In re Goodman, 29 USPQ2d 2010 (Fed. Cir. 1993). Since application claims 1 and 11 are anticipated by claims 1 and 11 of the patent, it is not patentably distinct from claims 1 and 11 of the patent.
Claim 2 of the current application corresponds to claim 2 of U.S. Patent No. 12,100,393.
Claim 3 of the current application corresponds to claim 3 of U.S. Patent No. 12,100,393.
Claim 4 of the current application corresponds to claim 4 of U.S. Patent No. 12,100,393.
Claim 5 of the current application corresponds to claim 5 of U.S. Patent No. 12,100,393.
Claim 6 of the current application corresponds to claim 6 of U.S. Patent No. 12,100,393.
Claim 7 of the current application corresponds to claim 7 of U.S. Patent No. 12,100,393.
Claim 8 of the current application corresponds to claim 8 of U.S. Patent No. 12,100,393.
Claim 9 of the current application corresponds to claim 9 of U.S. Patent No. 12,100,393.
Claim 10 of the current application corresponds to claim 10 of U.S. Patent No. 12,100,393.
Claim 12 of the current application corresponds to claim 12 of U.S. Patent No. 12,100,393.
Claim 13 of the current application corresponds to claim 13 of U.S. Patent No. 12,100,393.
Claim 14 of the current application corresponds to claim 14 of U.S. Patent No. 12,100,393.
Claim 15 of the current application corresponds to claim 15 of U.S. Patent No. 12,100,393.
Claim 16 of the current application corresponds to claim 16 of U.S. Patent No. 12,100,393.
Claim 17 of the current application corresponds to claim 17 of U.S. Patent No. 12,100,393.
Claim 18 of the current application corresponds to claim 18 of U.S. Patent No. 12,100,393.
Claim 19 of the current application corresponds to claim 19 of U.S. Patent No. 12,100,393.
Claim 20 of the current application corresponds to claim 20 of U.S. Patent No. 12,100,393.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more. Independent claims 1 and 11 relate to the statutory category of method/process and machine/apparatus. The independent claims recite “receiv(ing)… raw data describing an entity; determin(ing)… a plurality of execution elements from the raw data; determin(ing)… a data extrapolation of the plurality of execution elements; and generat(ing)… a directed graph as a function of the data extrapolation”.
The limitations of claims 1 and 11 of “receiv(ing)…”, ”determin(ing)…”, “determin(ing)…”, and “generat(ing)…” as drafted covers mental activity. More specifically, for claim 1, a human after receiving audio data from a user, can determine the from the information gathered what tasks need to be accomplished. From the information, the human can then predict or estimate what actions need to be performed to accomplish the tasks necessary. The actions are then put into a hierarchical graph/tree to show how the tasks will be accomplished.
This judicial exception is not integrated into a practical application. In particular, claims 1 and 11 recite the additional elements of “processor” and “memory” which are recited generally in the specification. For example, in paragraph [0009] of the as filed specification, there is a description of using a general purpose operating system. Accordingly, these additional elements do not integrate the abstract idea into a practical application, because they don impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As directed above with respect to the integration of the abstract idea into a practical application, the additional element of using a computer as a general computer is noted. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
With respect to claims 2 and 12, the claims relate to recognizing the audio data. The claims relate to the mental activity of a user recognizing what is being said. No additional limitations are present.
With respect to claims 3 and 13, the claims relate to determining how important the tasks are and base the actions on the importance. The claims relate to a mental activity of determining the importance of what needs to be done. No additional limitations are present.
With respect to claims 4 and 14, the claims relate to generating training data based on the audio data and the tasks that need to be performed. The training data is then used to train a learning model where, new information is input as new data is continuously received and updated. As new information is received, the tasks to be performed as based on the new information. The claims relate to a mental activity of using the latest information to determine the tasks to be performed and the actions to be taken. The additional element of training and using a machine-learning model does not integrate the abstract idea into a practical application. As stated above, mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
With respect to claims 5 and 15, the claims relate to determining who the end user is of the tasks to be performed based on characteristics of the user. The claims relate to a mental activity of determining who the tasks are being performed for based on their persona/characteristics. No additional limitations are present.
With respect to claims 6 and 16, the claims relate to determining who will be performing the tasks. The claims relate to determining who is qualified to perform the actions in order to complete the tasks. No additional limitations are present.
With respect to claims 7 and 17, the claims relate to determining what it will cost to perform the tasks . The claims relate to determining a monetary value or technical value to perform the tasks. No additional limitations are present.
With respect to claims 8 and 18, the claims relate to determining the confidence in predicting or estimating what actions need to be performed to accomplish the tasks necessary. The claims relate to a mental activity of determining how certain a human is on predicting what actions need to be taken. No additional limitations are present.
With respect to claims 9 and 19, the claims relate to generating training data based on the predictions of the actions that need to be taken to complete the tasks. The training data is then used to train a learning model where, new information is input as new data is continuously received and updated. As new information is received, the actions that need to be taken based on the new information. The claims relate to a mental activity of using the latest information to determine the actions to be taken. The additional element of training and using a machine-learning model does not integrate the abstract idea into a practical application. As stated above, mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
With respect to claims 10 and 20, the claims relate to converting the hierarchical graph/tree into linguistic terms using a language model. The claims relate to a mental activity of using the hierarchical graph/tree to determine the grammatical terms. The additional element of using a large language model does not integrate the abstract idea into a practical application. As stated above, mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-8, 10-18, and 20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipate by Kale et al. (WO 2018/034902).
Regarding Claim 1, Kale et al. discloses an apparatus of generating directed graph using raw data, the apparatus comprising: at least a processor (The machine 500 may include processors 504) (page 19, paragraph [0065]); and
a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor (The memory/storage 506 may include a memory 514, such as a main memory, or other memory storage, and a storage unit 516, both accessible to the processors 504 such as via the bus 502. The storage unit 516 and memory 514 store the instructions 510 embodying any one or more of the methodologies or functions described herein) (page 19, paragraph [0065]) to:
receive raw data describing an entity (In one example, a user may type the text input "Hi, can you find me a pair of red nikey shoes?") (page 28, paragraph [0093]);
determine a plurality of execution elements from the raw data (The parser sub-component 806 may also perform noun phrase chunking and discern from the longest parsed query fragment "red nike shoes" that the dominant object of the user's interest is shoes. That is, shoes are determined to be the object of the largest number of modifiers and are at the deepest level of a resulting chunking structure) (pages 28, paragraph [0095]);
determine a data extrapolation of the plurality of execution elements (The dominant object here is also described by modifiers ("red" and "nike") which the Named Entity Recognizer 810 may determine relate to a color and a brand, respectively) (page 28, paragraph [0095]); and
generate a directed graph as a function of the data extrapolation (The knowledge graph 808 may also use dominant (e.g., most frequently user-queried or most frequently occurring in an item inventory) attributes pertaining to that item category, and the dominant values for those attributes) (page 25, paragraph [0083]).
Regarding Claim 2, Kale et al. discloses the apparatus, wherein the memory contains the instructions configuring the at least a processor to analyze the raw data using automatic speech recognition (With reference to Figure 3 A, the illustrated components of the speech recognition component 210 are now described. A feature extraction component operates to convert raw- audio waveform to some-dimensional vector of numbers that represents the sound) (page 14, paragraph [0051]).
Regarding Claim 3, Kale et al. discloses the apparatus, wherein the memory contains the instructions configuring the at least a processor to: determine a weighted value of each of the plurality of execution elements (Note also that in this instance two attributes (color, brand) and corresponding attribute values (red, nike) are provided for the dominant object … The dialog manager 216 may decide as a result that the user's original query is sufficiently constrained that an appropriate prompt may be one or more item recommendations, rather than a question asking a user for additional constraints that would further narrow the subsequent search) (pages 28 and 29, paragraph [0097]) (Further, world knowledge or other potentially relevant external contextual information may adjust the weighting of prompt possibilities by dialog manager 216) (pages 35 and 35, paragraph [00119]); and
determine the data extrapolation as a function of the weighted value of each of the plurality of execution elements (Therefore, the NLU component 214 may consult the knowledge graph 808 to determine the most helpful attributes for this dominant object of user interest. The knowledge graph 808 may have information indicating that for the item category "shoes", the most helpful and/or frequently specified attributes are color, brand, and size, along with corresponding conditional probability values showing the relative correlation or association strength or conditional probability of importance of each in finding a relevant item) (page 29, paragraph [0097]).
Regarding Claim 4, Kale et al. discloses the apparatus, wherein the memory contains the instructions configuring the at least a processor to: generate element training data, wherein the element training data comprises correlations between exemplary raw data and exemplary execution elements (The speaker adaptation component allows clients of an STT system (e.g., speech recognition component 210) to customize the feature extraction component and/or the acoustic model component for each speaker/user. This can be important because most speech-to-text systems are trained on data from a representative set of speakers from a target region and typically the accuracy of the system depends heavily on how well the target speaker matches the speakers in the training pool) (pages 14 and 15, paragraph [0052]);
train an element machine-learning model using the element training data, wherein the element training data is iteratively updated through a feedback loop (The LM adaptation component operates to customize the language model component and the speech-to-text vocabulary with new words and representative sentences from a target domain, for example, inventory categories or user personas. This capability allows the artificial intelligence framework 128 to be scalable as new categories and personas are supported) (page 15, paragraph [0053]); and
determine the plurality of execution elements using the trained element machine-learning model (The speaker adaptation component allows the speech recognition component 210 (and consequently the artificial intelligence framework 128) to be robust to speaker variations by continuously learning the idiosyncrasies of a user's intonation, pronunciation, accent, and other speech factors, and apply these to the speech-dependent components, e.g,, the feature extraction component, and the acoustic model component) (pages 14 and 15, paragraph [0052]).
Regarding Claim 5, Kale et al. discloses the apparatus, wherein the memory contains the instructions configuring the at least a processor to: determine an end user of the plurality of execution elements (The context manager 218 manages the context and communication of a user with respect to the intelligent online personal assistant (or "bot") and the assistant's associated artificial intelligence. The context manager 218 retains a short term history of user interactions. A longer term history of user preferences may be retained in an identity service 222) (page 10, paragraph [0037]); and
determine the at least an operation datum as a function of a plurality of characteristics of the end user (An identity service 222 component operates to manage user profiles, for example explicit information in the form of user attributes, e.g., "name", "age", "gender", "geolocation", but also implicit information in forms such as "information distillates" such as "user interest", or "similar persona", and so forth) (page 10, paragraph [0039]).
Regarding Claim 6, Kale et al. discloses the apparatus, wherein the memory contains the instructions configuring the at least a processor to determine at least an executor of the plurality of execution elements (The context manager 218 manages the context and communication of a user with respect to the intelligent online personal assistant (or "bot") and the assistant's associated artificial intelligence. The context manager 218 retains a short term history of user interactions. A longer term history of user preferences may be retained in an identity service 222, described below. Data entries in one or both of these histories may include the relevant intent and all parameters and all related results of a given input, bot interaction, or turn of communication, for example) (page 10, paragraph [0037]).
Regarding Claim 7, Kale et al. discloses the apparatus, wherein the memory contains the instructions configuring the at least a processor to determine an execution token datum of the plurality of execution elements (The NER sub-component 810 may extract deeper information from parsed user input (e.g., brand names, size information, colors, and other descriptors) and help transform the user natural language query into a structured query comprising such parsed data elements. The NER sub-component may also tap into world knowledge to help resolve meaning for extracted terms. For example, a query for "a bordeaux" may more successfully determine from an online dictionary and encyclopedia that the query term may refer to an item category (wine), attributes (type, color, origin location), and respective corresponding attribute values (Bordeaux, red, France). Similarly, a place name (e.g., Lake Tahoe) may correspond to a given geographic location, weather data, cultural information, relative costs, and popular activities that may help a user find a relevant item. The structured query depth (e.g., number of tags resolved for a given user utterance length) may help the dialog manager 216 select what further action it should take to improve a ranking in a search performed by the search component 220) (page 26, paragraph [0086]).
Regarding Claim 8, Kale et al. discloses the apparatus, wherein the memory contains the instructions configuring the at least a processor to generate a confidence level of the data extrapolation (In Figure 1 1 A, the normalized and parsed user query has provided the item attribute/value tags of <color:red, brand:nike> for a dominant object of user interest "Shoes", as previously described. The knowledge graph 808 indicates there is a forty percent (0.4) correlation between "Shoes" and "Men' s Athletic Shoes", and that there is a forty percent (0,4) correlation between "Men' s Athletic Shoes" and "Brand", and a twenty percent (0.2) correlation between "Men' s Athletic Shoes" and "Color". There is also a thirty percent (0.3) correlation between "Men' s Athletic Shoes" and "Product Line", and various correlations for various item attribute values (e.g., "Air Jordan", "Kobe Bryant", and "Air Force 1") are known. Thus, whether based on inventory or user behavior, the as-yet unspecified query terms of "Men' s Athletic Shoes" and "Product Line" have significant associations with a successful search. The dialog manager 216 may therefore rank and prioritize the parameterization of these as-yet unspecified possibilities through user prompts according to their association or correlation values, or their relative positions in the knowledge graph 808 hierarchy, or a combination of both) (pages 33-34, paragraph [00113]).
Regarding Claim 10, Kale et al. discloses the apparatus, wherein the memory contains the instructions configuring the at least a processor to convert the directed graph into a plurality of linguistic terms (At 1308, the methodology may analyze the parsed input data to find matches between the dimensions of the knowledge graph 808 and the dominant object and the related parameters. At 1310, the methodology may aggregate the analysis results into a formal query for searching. At 1312, the methodology may optionally generate a user prompt or prompts for additional input data from the user) (page [00130]) using a large language model ( A language model component uses statistical models of grammar to define how words are put together in a sentence. Such models can include n-gram-based models or Deep Neural Networks built on top of word embeddings) (page 14, paragraph [0051]).
Regarding Claim 11, Kale et al. discloses a method of generating a directed graph using raw data, the method comprising: receiving, using at least a processor (The instructions 510 may also reside, completely or partially, within the memory 514, within the storage unit 516, within at least one of the processors 504 (e.g., within the processor' s cache memory), or any- suitable combination thereof, during execution thereof by the machine 500) (page 19, paragraph [0065]), raw data describing an entity (In one example, a user may type the text input "Hi, can you find me a pair of red nikey shoes?") (page 28, paragraph [0093]);
determining, using the at least a processor, a plurality of execution elements from the raw data (The parser sub-component 806 may also perform noun phrase chunking and discern from the longest parsed query fragment "red nike shoes" that the dominant object of the user's interest is shoes. That is, shoes are determined to be the object of the largest number of modifiers and are at the deepest level of a resulting chunking structure) (pages 28, paragraph [0095]);
determining, using the at least a processor, a data extrapolation of the plurality of execution elements (The dominant object here is also described by modifiers ("red" and "nike") which the Named Entity Recognizer 810 may determine relate to a color and a brand, respectively) (page 28, paragraph [0095]); and
generating, using the at least a processor, a directed graph as a function of the data extrapolation (The knowledge graph 808 may also use dominant (e.g., most frequently user-queried or most frequently occurring in an item inventory) attributes pertaining to that item category, and the dominant values for those attributes) (page 25, paragraph [0083]).
Claim 12 is rejected for the same reason as claim 2.
Claim 13 is rejected for the same reason as claim 3.
Claim 14 is rejected for the same reason as claim 4.
Claim 15 is rejected for the same reason as claim 5.
Claim 16 is rejected for the same reason as claim 6.
Claim 17 is rejected for the same reason as claim 7.
Claim 18 is rejected for the same reason as claim 8.
Claim 20 is rejected for the same reason as claim 10.
Allowable Subject Matter
Claims 9 and 19 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims and if the Double Patenting rejections and the 35 USC 101 rejections above are overcome.
Cited Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Zhang (US 10,929,440) discloses traditional Chinese medicine knowledge graph and establishment.
Engelberg et al. US 2023/0328096) discloses ontology0based risk propagation over digital twins.
Dembo et al. (US 2024/0104401) discloses classification standard for computer models to measure and manage radical risk using machine learning and scenario generation.
Gautam (US 2024/0378562) discloses intelligent substitution in process automation.
Chen et al. (US 2025/0147993) discloses graph and vector usage for automated QA system.
Raghavan et al. (US 2025/0328401) discloses quantum transformation based correlated relationship extraction for failure preemption and predictive analytics.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SATWANT K SINGH whose telephone number is (571)272-7468. The examiner can normally be reached Monday thru Friday 9:00 AM to 6:00 PM EST.
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/SATWANT K SINGH/ Primary Examiner, Art Unit 2653
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