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
Claims 1-5 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim 1
Step 1, This part of the eligibility analysis evaluates whether the claim falls within any statutory category. See MPEP 2106.03. The claim is a document search device/machine that performs at least one step. Thus, the claim is a machine, which is one of the statutory categories of invention (Step 1: YES).
Step 2A – Prong One: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
Limitation “wherein the analog circuit is configured to perform a product-sum operation of the neural network.” This limitation explicitly describes performing a product-sum operation. A product-sum operation involves multiplying values and summing results, which constitutes a mathematical calculation or formula. It falls within the groupings of abstract ideas defined as mathematical concepts.
Limitation “wherein the processing portion is configured to obtain a first distributed representation vector and a first weight of the first distributed representation vector from the input document using the neural network.” This limitation describes obtaining a first distributed representation vector and its associated weight using a neural network. Obtaining distributed representation vectors involves generating mathematical representations (embeddings) of data points. The limitation falls within the groupings of abstract ideas defined as mathematical concepts.
Limitation “wherein the processing portion is configured to extract related terms of a keyword extracted from words included in the input document based on a similarity degree with between the first distributed representation vector and a second distributed representation vector of words included in a plurality of pieces of reference text analysis data.” This limitation recites extracting terms based on a calculated similarity degree between two vectors. Determining a similarity degree requires mathematical calculation, such as measuring the distance or correlation between the vectors representing the keyword and the words in the reference data. The limitation falls within the groupings of abstract ideas defined as mathematical concepts.
Limitation: “wherein the processing portion is configured to obtain a second weight of the related terms, the second weight comprising a product of the first weight by the similarity degree.” This limitation describes obtaining a second weight that is calculated as a product of two variables. The phrase “comprising a product of the first weight by the similarity degree” explicitly recites a mathematical operation (multiplication). The limitation falls within the groupings of abstract ideas defined as mathematical concepts.
Step 2 – prong 2: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d).
A list of additional elements
a document search device, for searching a document related or similar to an input document, comprising a processing portion comprising an analog circuit and a neural network
wherein the processing portion is configured to execute the search of the document by using the first weight and the second weight
MPEP 2106.05(a) Improvements to the Functioning of a Computer or to Any Other Technology or Technical Field:
The additional element “comprising a processing portion comprising an analog circuit and a neural network” is not recited in a manner that improves the functioning of a computer or any other technology. Instead, the claim recites generic hardware configured to perform calculations inherent to the abstract idea of mathematical weighting and search. The specification does not demonstrate that this specific configuration solves a technical problem related to data processing or computing architecture itself; rather, it applies standard mathematical concepts using existing circuitry without adding functionality beyond implementing the abstract calculation.
MPEP 2106.05(b) Particular Machine:
The elements “analog circuit and a neural network” do not constitute a particular machine that integrates the judicial exception into a practical application. They are configured here in their ordinary capacity to execute the mathematical steps defined in the claim. They function merely as tools for performing the abstract idea of calculating weights and similarity degrees.
MPEP 2106.05(c) Particular Transformation:
The additional elements do not effect a transformation or reduction of a particular article to a different state or thing. The claim does not recite transforming any physical article, such as paper-to-digital or chemical-to-physical change. The "transformation" here is purely digital or virtual data manipulation (vectors and weights) within the memory of the device.
MPEP 2106.05(e) Other Meaningful Limitations:
The additional elements do not provide other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment. The “processing portion configured to execute the search” merely applies the calculated weights to return results. It simply links the abstract idea of weighted similarity calculation to the execution of a document search, which is a generic field of application for such calculations.
MPEP 2106.05(g) Insignificant Extra-Solution Activity:
The execution of the search using the weights qualifies as insignificant extra-solution activity. This step occurs after the core mathematical abstraction (the calculation of weights and similarity degrees) has been completed. It is simply displaying or selecting results based on the abstract data.
MPEP 2106.05(h) Field of Use and Technological Environment:
The additional elements amount to merely linking the use of the judicial exception to a particular technological environment. The claim does not recite how the document search technology itself is improved; it only applies mathematical concepts within the context of searching documents, which is a conventional application for weighted search algorithms.
Step 2b, Examine the additional elements individually and as an ordered combination—to see if they provide an inventive concept that adds "significantly more" than the exception itself. The analog circuit and neural network are configured to execute standard mathematical operations (product-sum, vector generation, similarity calculation). There is no unconventional arrangement of these components beyond their standard implementation for data processing. The claim does not describe a specific, non-generic solution to a technical problem that distinguishes it from the prior art. The steps are recited at a high level of generality (e.g., standard processor or analog hardware). The combination relies on generic functions to implement the abstract idea without adding significant limitations that overcome the preemption concern. The additional elements, taken individually and as an ordered combination, do not amount to significantly more than the judicial exception. The claim simply recites implementing the abstract idea of mathematical calculation and similarity searching using generic hardware and performing a search step after the calculation. Consequently, the claim does not integrate the judicial exception into a practical application or provide an inventive concept that transforms the claim into patent-eligible subject matter. The claim is ineligible under 35 U.S.C. § 101.
Claim 2 recites “The document search device according to claim 1, wherein the first distributed representation vector and the second distributed representation vector are obtained through a machine learning of distributed representation of a word included in the plurality of pieces of reference text analysis data.” This limitation describes how the vectors are generated using machine learning. Machine learning is a conventional technique used to process data and generate mathematical models, which falls under well-understood, routine, and conventional activity previously known in the field. While it specifies the method for obtaining the vectors, it does not improve the functioning of the computer or provide a specific technical solution that transforms the abstract idea into a patent-eligible application; it merely defines the algorithmic steps used to implement the mathematical concept of vector representation. Therefore, this limitation fails to add an inventive concept sufficient to rescue the claim from ineligibility.
Claim 3 recites “The document search device according to claim 1, wherein the first distributed representation vector is obtained from a keyword extracted from the input document.” This limitation restricts the data source for the vectors to keywords derived from the user's input. The claim does not describe a technological improvement to how the search device functions. The claim does not provide an inventive concept and the claim remains ineligible.
Claim 4 recites “The document search device according to claim 1, wherein the processing portion is configured to output the first weight and the second weight, and wherein the processing portion is configured to receive a change to value of one or more of the first weight and the second weight.”. Receiving a change in values and outputting results are generic computer functions that do not integrate the abstract idea into a practical application. The claim does not improve the underlying technology of the device or solve a specific technical problem in a non-conventional way, they do not amount to significantly more than the judicial exception found in claim 1.
Claim 5 recites “The document search device according to claim 2, wherein the machine learning uses the neural network.” Without introducing a novel configuration of the neural network or specific improvements to its functionality that go beyond standard implementation, this addition does not provide an inventive concept.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure
U.S. Patent 11030539 – Arfa discloses a method includes receiving a request to identify a word representing a target concept that is in a first relationship with a particular concept such that the first relationship is analogous to a second relationship in which a first reference concept is with a second reference concept, accessing a table of word vector relationships, looking up a particular word vector, a first reference word vector, and a second reference word vector, determining an imaginary vector such that a first vector from the first reference word vector to the second reference word vector is equal to a second vector from the particular word vector to the imaginary vector, selecting a target word vector closest to the imaginary vector, identifying a target n-gram corresponding to the target word vector, and sending a response message comprising the target n-gram.
U.S. Patent 10956822 – Kern discloses systems and methods of managing regulatory questions. The systems and methods receive a question having words and phrases. The systems and methods identify keywords in the question using a knowledgebase. The systems and methods determine closely related questions based on the identification, the closely related questions having answers associated with each question of the closely related questions. The systems and methods perform machine learning on the answers of the determined closely related questions.
U.S. Patent 10621237 – Rathnavelu discloses approaches provide for analyzing document data to provide contextual overlays. For example, an application executing on a computing device (or at least in communication with the computing device) can analyze document data to determine a set of keywords based on features extracted from the document data. The keywords can be used to query an index of websites based on a relevance function in order to determine websites that are most relevant to the text identified from the document, at least some of which can be analyzed using a search engine to identify contextual information in the websites associated with the document. Thereafter, the contextual information can be provided for display with the document.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HAU HAI HOANG whose telephone number is (571)270-5894. The examiner can normally be reached 1st biwk: Mon-Thurs 7:00 AM-5:00 PM; 2nd biwk: Mon-Thurs: 7:00 am-5:00pm, Fri: 7:00 am - 4:00pm.
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HAU HAI. HOANG
Primary Examiner
Art Unit 2154
/HAU H HOANG/Primary Examiner, Art Unit 2154
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