DETAILED ACTION
Remarks
This Office Action is in response to the application 18/725464 filed on 28 June 2024.
Claims 1-8 and 10-21 have been examined.
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 .
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 1-8 and 10-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
As to claims 1, 8, and 10, these claims recite obtaining, based on the first information, a first hidden vector corresponding to the current dialog state. The term “vector” is interpreted according to its meaning in machine learning, which is a numerical representation of data points that express different types of data as an array of numbers that machine learning (ML) models can process1. The claimed “obtaining” of a first hidden vector is directly akin to saying calculating a first hidden vector, and this limitation amounts to no more than mathematical calculation(s). Hence, this limitation is an abstract idea under the “Mathematical Concepts” grouping. Furthermore, the claim does not specify nor place any limits upon the claimed “first information,” “current dialog state,” and the “first hidden vector.” Under the broadest reasonable interpretation (BRI), this limitation encompasses a simple case, i.e. a small amount of information and a simple vector. For such a simple case, a human could, with the aid of pencil and paper, mentally calculate a first hidden vector in the manner claimed. Hence, this limitation may alternatively be deemed an abstract idea under the “Mental Processes” grouping.
These claims also recite simulating, based on the first hidden vector, K single-action dialogs to obtain the dialog policy, K being a positive integer. Under the BRI, K is equal to one, and hence the claim only requires simulating one single-action dialog. The top half of Applicant’s Fig. 1 depicts a couple of single-action dialogs, and para. 0025 of Applicant’s specification explains Fig. 1 in more detail. With the aid of pencil and paper, a human could simulate a single-action dialog in the manner claimed. Hence, this limitation is an abstract idea under the “Mental Processes” grouping. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. Other than the abstract idea, the claims recite the following:
a) acquiring first information for characterizing a current dialog state;
b) a memory for storing a computer program;
c) a processor;
d) a non-transitory computer readable storage medium having stored thereon a program for execution by a processor.
Limitation (a) amounts to no more than mere data gathering, which has been deemed by the courts to be insignificant extra-solution activity. See MPEP 2106.05(g). Limitations (b) through (d) are recited at a high level of generality, i.e. as generic computer components performing generic computing functions. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Looking at the additional elements as a whole adds nothing beyond the additional elements considered individually—they still represent insignificant extra-solution activity and/or generic computer implementation. Hence, the claim as a whole, looking at the additional elements individually and in combination, does not integrate the abstract idea into a practical application. The claim is 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. Limitation (a) amounts to no more than mere data gathering, which has been deemed by the courts to be insignificant extra-solution activity. See MPEP 2106.05(g). In addition, the courts have deemed receiving data to be well-understood, routine, and conventional activity, as in the following cases: Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015) (storing and retrieving information in memory). See MPEP 2106.05(d)(II). As discussed above with respect to integration of the abstract idea into a practical application, additional elements (b) through (d) amount to no more than mere field of use limitations and instructions to apply the exception using generic computer components. Mere instructions to apply an exception using conventional computer components and functions cannot provide an inventive concept. Looking at the additional elements as a whole adds nothing beyond the additional elements considered individually—they still represent insignificant extra-solution activity; well-understood, routine, and conventional subject matter; and/or generic computer implementation. Hence, the claim as a whole, looking at the additional elements individually and in combination, does not amount to significantly more than the abstract idea. These claims are not patent eligible.
As to dependent claims 2, 11, and 17, these claims merely provide certain examples of the claimed “first information” upon which the invention can be applied. These claims amount to mere description of a field of use and/or technological environment in which to practice the invention, which cannot be deemed a practical application nor significantly more than the abstract idea. See MPEP 2106.05(h).
As to dependent claims 3-4, 6-7, 12-13, 15-16, 18-19, and 21, these claims recite inputting the first hidden vector into first and second models to obtain the dialog policy. These claims do not specify nor place any limits upon the first and second models, other than describing the first model as “a discrete policy model” and the second model as “a world model simulating a user behavior.” The plain meaning of the term “model” is “a system of postulates, data, and inferences presented as a mathematical description of an entity or state of affairs2” and the limitations of these claims amount to no more than a series of mathematical operations. Hence, these claims are an abstract idea under the “Mathematical Concepts” grouping.
As to dependent claims 5, 14 and 20, these claims recite inputting hidden vectors into a third model implemented by a full-connected feed-forward network. This limitation is recited at a high level of generality and amounts to mere instructions to apply the abstract idea on a general purpose computer, which cannot provide a practical application nor an inventive concept. See MPEP 2106.05(f).
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 and 2 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Shu et al. (Shu, L., Xu, H., Liu, B. and Molino, P., 2019, November. Modeling multi-action policy for task-oriented dialogues. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) (pp. 1304-1310). hereinafter referred to as Shu).
As to claim 1, Shu teaches a method for acquiring a dialog policy, comprising:
acquiring first information for characterizing a current dialog state (Shu Section 2 “Methodology”: the system inputs the current-turn dialogue state);
obtaining, based on the first information, a first hidden vector corresponding to the current dialog state (Shu Section 2 “Methodology”: hidden state vector hE.); and
simulating, based on the first hidden vector, K single-action dialogs to obtain the dialog policy, K being a positive integer (Shu Section 2 “Methodology”: the system outputs a series of single actions as part of the tuple (c; a; s), where “a” represents a single action).
As to claim 2, Shu teaches wherein the first information comprises at least one of:
a characterization result of a returned entity of a query;
a last system action before the current dialog state;
a last user action before the current dialog state;
a state of a request slot of a user; or
a state of a notification slot of a system (Shu Section 2 “Methodology”: the system inputs the current-turn dialogue state. Which “contains policy actions from the previous turn, user dialogue acts from the current turn, user requested slots, the user informed slots, the agent requested slots and agent proposed slots.).
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.
Claims 8, 10, 11, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Shu et al. (Shu, L., Xu, H., Liu, B. and Molino, P., 2019, November. Modeling multi-action policy for task-oriented dialogues. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) (pp. 1304-1310). hereinafter referred to as Shu).
As to claim 8, Shu teaches:
acquiring first information for characterizing a current dialog state (Shu Section 2 “Methodology”: the system inputs the current-turn dialogue state);
obtaining, based on the first information, a first hidden vector corresponding to the current dialog state (Shu Section 2 “Methodology”: hidden state vector hE.); and
simulating, based on the first hidden vector, K single-action dialogs to obtain the dialog policy, K being a positive integer (Shu Section 2 “Methodology”: the system outputs a series of single actions as part of the tuple (c; a; s), where “a” represents a single action).
Shu does not appear to explicitly disclose an apparatus for acquiring a dialog policy, comprising: a memory for storing a computer program; and a processor; wherein the processor is configured to execute the computer program stored in the memory.
However, Shu teaches that the code for implementing their solution is available online at a certain URL (see Shu page 1304, footnote one), and that code is “tested on macOS 10.14.6 with Python 2.7.15” (see Shu second-to-last page). macOS is a well-known computer operating system and Python is a well-known computer language. Hence, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention that Shu’s system is implemented using a computing apparatus comprising a memory storing a computer program for execution by a processor.
As to claim 10, Shu teaches:
acquiring first information for characterizing a current dialog state (Shu Section 2 “Methodology”: the system inputs the current-turn dialogue state);
obtaining, based on the first information, a first hidden vector corresponding to the current dialog state (Shu Section 2 “Methodology”: hidden state vector hE.); and
simulating, based on the first hidden vector, K single-action dialogs to obtain the dialog policy, K being a positive integer (Shu Section 2 “Methodology”: the system outputs a series of single actions as part of the tuple (c; a; s), where “a” represents a single action).
Shu does not appear to explicitly disclose a non-transitory computer readable storage medium having stored thereon a program that, when executed by a processor, causes the processor to implement operations.
However, Shu teaches that the code for implementing their solution is available online at a certain URL (see Shu page 1304, footnote one), and that code is “tested on macOS 10.14.6 with Python 2.7.15” (see Shu second-to-last page). macOS is a well-known computer operating system and Python is a well-known computer language. Hence, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention that Shu’s system is implemented using a computer readable storage medium having stored thereon a program for execution by a processor.
As to claim 11, Shu teaches wherein the first information comprises at least one of:
a characterization result of a returned entity of a query;
a last system action before the current dialog state;
a last user action before the current dialog state;
a state of a request slot of a user; or
a state of a notification slot of a system (Shu Section 2 “Methodology”: the system inputs the current-turn dialogue state. Which “contains policy actions from the previous turn, user dialogue acts from the current turn, user requested slots, the user informed slots, the agent requested slots and agent proposed slots.).
As to claim 17, see the rejection of claim 2 above.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to UMAR MIAN whose telephone number is (571)270-3970. The examiner can normally be reached Monday to Friday, 10 am to 6:30 pm.
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/Umar Mian/
Primary Examiner, Art Unit 2163
1 Bergmann, David and Strykyer, Cole. "What is vector embedding?" Published 12 June 2024 by IBM. Accessed 28 August 2025 from https://www.ibm.com/think/topics/vector-embedding
Teaches on page 1 that "Vector embeddings are numerical representations of data points that express different types of data, including nonmathematical data such as words or images, as an array of numbers that machine learning (ML) models can process."
2 https://www.merriam-webster.com/dictionary/model
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