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 .
Specification
The amendment filed 11 July 2025 is objected to under 35 U.S.C. 132(a) because it introduces new matter into the disclosure. 35 U.S.C. 132(a) states that no amendment shall introduce new matter into the disclosure of the invention. The added material which is not supported by the original disclosure is as follows: page 2 line 55- page 13 line 398, were not part of the original disclosure, additionally the articles are published after the filing date of the application thus it could not have been available before the filing of the application to be part of the specification. Applicant is required to cancel the new matter in the reply to this Office Action.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-48 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the enablement requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention.
Claims 1-48 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the enablement requirement. When determining that the enablement requirement has not been met, the factors to be considered include but are not limited to:
1. the breadth of the claims
2. the nature of the invention,
3. The state of the prior art,
4. The level of one of ordinary skill,
5. The level of predictability in the art,
6. The amount of direction provided by the inventor,
7. The existence of working examples, and
8. The quantity of experimentation needed to make or use the invention based on the content of the disclosure.
The claim(s) contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention. The claims cites “a decision tree algorithm comprising: target variables that define key attributes of innovation that can be non-data and data-types; proximal variables that are approximated attributes of target variables; nodes that configured to train and create predictive modes; and trains on innovation datasets containing categories and classifications that can be non-data and data types”, “target variables are innovation in categories”, “target variables are innovations in classifications”, “proximal variables are innovation in categories”, “proximal variables are innovations in classifications”, proximal variables share attributes with target variables”, “predictive models are combined”, “nodes have defined parameters”, “wherein nodes have approximated parameters”, “nodes are in a specific order of operation”, “nodes maintain specific order throughout cycles”, “nodes that are configured to multiple decision algorithms”, “nodes that are configured to follow a specific pattern”, “nodes that intersect target variables and proximal variables”, “nodes that are independent”, “nodes that are conditional”, “decision tree algorithms further comprising an encoder that encrypts the datasets and models”, “decision tree algorithms further comprising an decoder configured to decipher the encoder”, “decision tree algorithms further comprising reinforced learning and training on datasets”, ““decision tree algorithms further comprising deep learning and practicing on datasets”, “decision tree algorithms perform their functionalities in a digital platform business model”, “decision tree algorithms comprising a digital platform business model with multiple parties interacting”, “decision tree algorithms comprising a digital platform business model with networked ecosystems of parties interacting”. However, the specifications fails to give any directions on how a person or computer would perform any of the claim limitations. For example it’s not clear how a decision tree is trained, wherein the data sets for training a decision tree comes from and how they are derived from data, what are the target variables that define key attributes of innovations and how they are recognized by a person or system, what is meant by non-data and data types, how are key attributes of innovation determined, how are proximal variables determined, how are nodes configured to train and create predictive models, are the predictive models trained by nodes different from the nodes of the decision tree itself, what exactly are the categories and classifications of innovations and how they differ, how are predictive models combined by a decision tree, how is a node of a decision configured to multiple decision tree algorithms, how is an encoder and decoder integrated into a decision tree algorithm, is the decoder decrypting the encoder or the data from the encoder and what is the process for doing either option, how is reinforcement learning combined with the decision trees algorithm and how is a decision tree algorithm include a digital business model as a decision tree is a learning model. There is no support in the instant specification for any of the limitations. The is also no explanation, example or directions or how any of the limitation are performed by person or system. When considering the claims and the wands factors the examiner finds that claims and specification lack any direction, instructions, or examples on how the above limitation is accomplished, it does not meet wands factor 6 and 7 and without adequate instructions, directions, and working examples it would require under experimentation (Wands factor 8) for one to make or use the invention.
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 1-48 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter because claims 1 and 25 claim are directed to decision tree algorithms which are algorithms in the form of software with no structure, thus the claims are directed to software per se.
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.
The factual inquiries 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 1-17, 24-41, and 46-48 are rejected under 35 U.S.C. 103 as being unpatentable over Johnson (“Disruptive Innovation and Digital Transformation: 21st Century New Growth Engines”) in view of Rojas-Cordova et al. – (“Predicting Business Innovation Intention Based on Perceived Barriers: A Machine Learning Approach” – hereinafter Rojas-Cordova).
Regarding claim 1, Johnson discloses categories and classification that can be non-data and data types; target variables defining key attributes of innovations that can be non-data and data types; proximal variables are approximated attributes of target variables (In light of the 112 rejections supra, the claim is being interpreted as best understood by the examiner in the light of the prior art. Johnson chapter 1, fig. 1.2, table 1.1 and page 8 teaches categories and classifications that can be non-data type and data types. Any of the variables are target variables and proximal variables as it based on tracked data and megatrends.)
However, Johnson does not teach training a decision tree, using datasets, and nodes that are configured to train and create predictive models.
Rojas-Cordova et al. discloses Decision Tree Algorithms (decision tree algorithms, pg 4 para 2) comprising Training on innovation datasets containing Categories and Classifications that can be non-data and data types (We used a machine learning approach to assess this contingent model, in particular, the decision tree classification technique. This technique is a hierarchical structure consisting of nodes and directed edges that “solves a classification problem by asking a series of carefully crafted questions about the attributes of the test record”. In general, this study procedure is an instance of the use of data mining and symmetry-based learning concepts for particular classification and subsequent prediction, pg 2 para 3, in the decision trees method, an algorithm is used to divide a dataset into categories belonging to the response variable, builds decision trees from a collection of training data using the notion of information entropy, pg 4 para 3, used a machine learning approach applied to business innovation data as a way to explore the barriers that exist for this behavior, and their results confirm the usefulness of data science to evaluating conceptual propositions in business, pg 7 para 1); and Nodes that are configured to train and create predictive models (We used a machine learning approach to assess this contingent model, in particular, the decision tree classification technique. This technique is a hierarchical structure consisting of nodes and directed edges that “solves a classification problem by asking a series of carefully crafted questions about the attributes of the test record”, pg 2 para 3, A decision tree is an inverted tree-shaped model made up of a set of nodes intended to decide on values affiliated to a class, the technique is useful for predictive modeling, i.e., anticipating the class label of unknown records, in this case, the perceived barriers to business innovation intention in small and medium-sized (SME) and large-sized organizations, pg 4 para 2).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Johnson in view of Rojas-Cordova et al. in order to identify target variables, proximal variables, and categories and classifications of innovations and then implementing them in the decision tree as both reference deal with predicting business innovations, and the benefit of doing so it creates a system that does it faster and more efficiently.
In regards to claim 2, Johnson in view of Rojas-Cordova disclose the decision Tree Algorithms in claim 1, wherein Target Variables are innovations in categories. (Johnson chapter 1, fig. 1.2, table 1.1 and page 8 teaches categories and classifications that can be non-data type and data types. As well as target variables and proximal variables being tracked data and megatrends.)
In regards to claim 3, Johnson in view of Rojas-Cordova disclose the decision Tree Algorithms in claim 1, wherein Target Variables are innovations in classifications. (Johnson chapter 1, fig. 1.2, table 1.1 and page 8 teaches categories and classifications that can be non-data type and data types. As well as target variables and proximal variables being tracked data and megatrends.)
In regards to claim 4, Johnson in view of Rojas-Cordova disclose the decision Tree Algorithms in claim 1, wherein Proximal Variables are innovations in categories. (Johnson chapter 1, fig. 1.2, table 1.1 and page 8 teaches categories and classifications that can be non-data type and data types. As well as target variables and proximal variables being tracked data and megatrends.)
In regards to claim 5, Johnson in view of Rojas-Cordova disclose the decision Tree Algorithms in claim 1, wherein Proximal Variables are innovations in classifications. (Johnson chapter 1, fig. 1.2, table 1.1 and page 8 teaches categories and classifications that can be non-data type and data types. As well as target variables and proximal variables being tracked data and megatrends.)
In regards to claim 6, Johnson in view of Rojas-Cordova disclose the decision Tree Algorithms in claim 1, wherein Proximal Variables share attributes with Target Variables. (Johnson chapter 1, fig. 1.2, table 1.1 and page 8 teaches categories and classifications that can be non-data type and data types. As well as target variables and proximal variables being tracked data and megatrends.)
In regards to claim 7, Johnson in view of Rojas-Cordova disclose the decision Tree Algorithms in claim 1, wherein predictive models can be combined. (Rojas-Cordova et al. fig. 3 teaches nodes that independent (parent nodes), dependent (child nodes), specific patterns and order (the order of the nodes is the pattern and order), the paths of decision tree are decision tree algorithms, the various nodes interest each other via edges, each nodes has either a defined parameter or range (approximation), and each node is a model is combined to form the decision tree.)
In regards to claim 8, Johnson in view of Rojas-Cordova disclose the decision Tree Algorithms in claim 1, wherein Nodes have defined parameters. (Rojas-Cordova et al. fig. 3 each nodes has either a defined parameter or range (approximation), for example less or equal to 0.5 and another node being greater than 0.5.)
In regards to claim 9, Johnson in view of Rojas-Cordova disclose the decision Tree Algorithms in claim 1, wherein Nodes have approximated parameters. (Rojas-Cordova et al. fig. 3 each nodes has either a defined parameter or range (approximation), for example less or equal to 0.5 and another node being greater than 0.5, wherein the range is an approximation.)
In regards to claim 10, Johnson in view of Rojas-Cordova disclose the decision Tree Algorithms in claim 1, wherein Nodes are in a specific order of operation. (Rojas-Cordova et al. fig. 3 teaches nodes that independent (parent nodes), dependent (child nodes), specific patterns and order (the order of the nodes is the pattern and is specific order of the connections).)
In regards to claim 11, Johnson in view of Rojas-Cordova disclose the decision Tree Algorithms in claim 1, wherein Nodes maintain specific order throughout cycles. (Rojas-Cordova shows figs. 2 and 3 with a specific order of the nodes in the tree.)
In regards to claim 12, Johnson in view of Rojas-Cordova disclose the decision Tree Algorithms in claim 1, further comprising Nodes that are configured to multiple decision tree algorithms. (Rojas-Cordova teaches each node has decision it makes choices on wherein the choices are the decision tree algorithms.)
In regards to claim 13, Johnson in view of Rojas-Cordova disclose the decision Tree Algorithms in claim 1, further comprising Nodes that are configured to follow a specific pattern. (Rojas-Cordova et al. fig. 3 teaches nodes that independent (parent nodes), dependent (child nodes), specific patterns and order (the order of the nodes is the pattern and order).)
In regards to claim 14, Johnson in view of Rojas-Cordova disclose the decision Tree Algorithms in claim 1, further comprising Nodes that are configured to multiple decision tree algorithms. (Rojas-Cordova teaches each node has decision it makes choices on wherein the choices are the decision tree algorithms.)
In regards to claim 15, Johnson in view of Rojas-Cordova disclose the decision Tree Algorithms in claim 1, further comprising Nodes that intersect Target Variables and Proximal Variables. (Rojas-Cordova et al. fig. 3 teaches a decision tree with nodes wherein the various nodes intersect each other via edges, thus the target variables and proximal variable nodes intersect.)
In regards to claim 16, Johnson in view of Rojas-Cordova disclose the decision Tree Algorithms in claim 1, further comprising Nodes that can be independent. (Rojas-Cordova et al. fig. 3 teaches nodes that independent (parent nodes).)
In regards to claim 17, Johnson in view of Rojas-Cordova disclose the decision Tree Algorithms in claim 1, further comprising Nodes that can be conditional. (Rojas-Cordova et al. fig. 3 teaches nodes that independent (parent nodes), dependent (child nodes, which are conditional as they are based on parent nodes).)
In regards to claim 22, Johnson in view of Rojas-Cordova disclose the decision Tree Algorithms in claim 1, therein perform their functionalities in a digital platform business model. (Johnson page 57 teacehs digital platform business model with multiple parties interacting and networked ecosystems wherein it cites “Fortunately, companies with the right capabilities can engage with customers through Internet of Things (IoT) devices, applications, and platforms. "The 2015 Accenture Digital Consumer Survey found that by 2020, nearly half of consumers will own a connected Internet of Things (IoT) device, with strongest demand for home cameras and security, smart watches and fitness devices" (World Economic Forum, 2015). IoT platforms enable these applications to communicate and to transmit across a spectrum of connected users, devices, and systems as well as a broader networked ecosystem. Billions of internet-Of-Things (IoT) devices that connect products and services also collect vast amounts of information on customers, as well as usage patterns (see Table 4.2. for five categories of IoT devices). "After machine-learning algorithms analyze this "digital exhaust," a company's offe1ings can be automatically adjusted to reflect the findings and even tailored to individuals" (Hagui et al. 2019). This data can be used across digital products, services, and business models to serve a market with economies of-scale and economies-of-scope, as well as to achieve valuable network effects.”)
In regards to claim 23, Johnson in view of Rojas-Cordova disclose the decision Tree Algorithms in claim 14, further comprising a digital platform business model with multiple parties interacting. (Johnson page 57 teaches digital platform business model with multiple parties interacting and networked ecosystems wherein it cites “Fortunately, companies with the right capabilities can engage with customers through Internet of Things (IoT) devices, applications, and platforms. "The 2015 Accenture Digital Consumer Survey found that by 2020, nearly half of consumers will own a connected Internet of Things (IoT) device, with strongest demand for home cameras and security, smart watches and fitness devices" (World Economic Forum, 2015). IoT platforms enable these applications to communicate and to transmit across a spectrum of connected users, devices, and systems as well as a broader networked ecosystem. Billions of internet-Of-Things (IoT) devices that connect products and services also collect vast amounts of information on customers, as well as usage patterns (see Table 4.2. for five categories of IoT devices). "After machine-learning algorithms analyze this "digital exhaust," a company's offe1ings can be automatically adjusted to reflect the findings and even tailored to individuals" (Hagui et al. 2019). This data can be used across digital products, services, and business models to serve a market with economies of-scale and economies-of-scope, as well as to achieve valuable network effects.”)
In regards to claim 24, Johnson in view of Rojas-Cordova disclose the decision Tree Algorithms in claim 14, further comprising a digital platform business model with networked ecosystems of parties interacting. (Johnson page 57 teaches digital platform business model with multiple parties interacting and networked ecosystems wherein it cites “Fortunately, companies with the right capabilities can engage with customers through Internet of Things (IoT) devices, applications, and platforms. "The 2015 Accenture Digital Consumer Survey found that by 2020, nearly half of consumers will own a connected Internet of Things (IoT) device, with strongest demand for home cameras and security, smart watches and fitness devices" (World Economic Forum, 2015). IoT platforms enable these applications to communicate and to transmit across a spectrum of connected users, devices, and systems as well as a broader networked ecosystem. Billions of internet-Of-Things (IoT) devices that connect products and services also collect vast amounts of information on customers, as well as usage patterns (see Table 4.2. for five categories of IoT devices). "After machine-learning algorithms analyze this "digital exhaust," a company's offe1ings can be automatically adjusted to reflect the findings and even tailored to individuals" (Hagui et al. 2019). This data can be used across digital products, services, and business models to serve a market with economies of-scale and economies-of-scope, as well as to achieve valuable network effects.”)
In regards to claim 25 is the similar to that of claim 1 and thus is rejected using the same reasoning found in claim 1.
In regards to claim 26 is the similar to that of claim 2 and thus is rejected using the same reasoning found in claim 2.
In regards to claim 27 is the similar to that of claim 3 and thus is rejected using the same reasoning found in claim 3.
In regards to claim 28 is the similar to that of claim 4 and thus is rejected using the same reasoning found in claim 4.
In regards to claim 29 is the similar to that of claim 5 and thus is rejected using the same reasoning found in claim 5.
In regards to claim 30 is the similar to that of claim 6 and thus is rejected using the same reasoning found in claim 6.
In regards to claim 31 is the similar to that of claim 7 and thus is rejected using the same reasoning found in claim 7.
In regards to claim 32 is the similar to that of claim 8 and thus is rejected using the same reasoning found in claim 8.
In regards to claim 33 is the similar to that of claim 9 and thus is rejected using the same reasoning found in claim 9.
In regards to claim 34 is the similar to that of claim 10 and thus is rejected using the same reasoning found in claim 10.
In regards to claim 35 is the similar to that of claim 11 and thus is rejected using the same reasoning found in claim 11.
In regards to claim 36 is the similar to that of claim 12 and thus is rejected using the same reasoning found in claim 12.
In regards to claim 37 is the similar to that of claim 13 and thus is rejected using the same reasoning found in claim 13.
In regards to claim 38 is the similar to that of claim 14 and thus is rejected using the same reasoning found in claim 14.
In regards to claim 39 is the similar to that of claim 15 and thus is rejected using the same reasoning found in claim 15.
In regards to claim 40 is the similar to that of claim 16 and thus is rejected using the same reasoning found in claim 16.
In regards to claim 41 is the similar to that of claim 17 and thus is rejected using the same reasoning found in claim 17.
In regards to claim 46 is the similar to that of claim 22 and thus is rejected using the same reasoning found in claim 22
In regards to claim 47 is the similar to that of claim 23 and thus is rejected using the same reasoning found in claim 23.
In regards to claim 48 is the similar to that of claim 24 and thus is rejected using the same reasoning found in claim 24.
Claims 18-20 and 42-44 are rejected under 35 U.S.C. 103 as being unpatentable over Johnson (“Disruptive Innovation and Digital Transformation: 21st Century New Growth Engines”) in view of Rojas-Cordova et al. – (“Predicting Business Innovation Intention Based on Perceived Barriers: A Machine Learning Approach” – hereinafter Rojas-Cordova) and further in view of Xiong et al. (“Learning Decision Trees with Reinforcement learning” – hereinafter Xiong).
In regards to claim 18, Johnson in view of Rojas-Cordova disclose the decision Tree Algorithms in claim 1, but fails to disclose it further comprising an encoder that encrypts the datasets and models.
Xiong discloses an encoder that encrypts the datasets and models. (Xiong abstract discloses decision trees using reinforcement learning and page 2 section 3 teaches encoded data and models. Also as there is encoded data there must also be decoded data.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Johnson in view of Rojas-Cordova with that of Xiong in order to allow for a decision tree using reinforcement learning along with a decoder and encoder both Rojas-Cordova deals with decision trees and it provides the benefit or have secure data.
In regards to claim 19, Johnson in view of Rojas-Cordova disclose the decision Tree Algorithms in claim 1, but fails to disclose it further comprising a decoder configured to decipher the encoder.
Xiong discloses an encoder that encrypts the datasets and models. (Xiong abstract discloses decision trees using reinforcement learning and page 2 section 3 teaches encoded data and models. Also, as there is encoded data there must also be decoded data.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Johnson in view of Rojas-Cordova with that of Xiong in order to allow for a decision tree using reinforcement learning along with a decoder and encoder both Rojas-Cordova deals with decision trees and it provides the benefit or have secure data.
In regards to claim 20, Johnson in view of Rojas-Cordova disclose the decision Tree Algorithms in claim 1, but fails to disclose it further comprising reinforced learning and training on datasets.
Xiong discloses reinforced learning and training on datasets. (Xiong abstract discloses decision trees using reinforcement learning and page 2 section 3 teaches encoded data and models. Also, as there is encoded data there must also be decoded data.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Johnson in view of Rojas-Cordova with that of Xiong in order to allow for a decision tree using reinforcement learning along with a decoder and encoder both Rojas-Cordova deals with decision trees and it provides the benefit or have secure data.
In regards to claim 42 is the similar to that of claim 18 and thus is rejected using the same reasoning found in claim 18.
In regards to claim 43 is the similar to that of claim 19 and thus is rejected using the same reasoning found in claim 19.
In regards to claim 44 is the similar to that of claim 20 and thus is rejected using the same reasoning found in claim 20.
Claims 21 and 45 are rejected under 35 U.S.C. 103 as being unpatentable over Johnson (“Disruptive Innovation and Digital Transformation: 21st Century New Growth Engines”) in view of Rojas-Cordova et al. – (“Predicting Business Innovation Intention Based on Perceived Barriers: A Machine Learning Approach” – hereinafter Rojas-Cordova) and further in view of Yang et al. (“Deep Neural Decision Trees” – hereinafter Yang).
In regards to claim 21, Johnson in view of Rojas-Cordova disclose the decision Tree Algorithms in claim 1, but fails to disclose it further comprising deep learning and practicing on datasets.
Yang discloses deep learning and practicing on datasets. (Yang abstract teaches combining deep learning with decision trees wherein it trained (practicing) on datasets.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Johnson in view of Rojas-Cordova with that of Yang in order to allow for a decision tree using deep learning as both Rojas-Cordova and Yang deal with the use of decision trees and Yang combines decision trees with deep learning as decision trees provides the benefit of being easy interpreted and also popular as disclosed by the Yang in the abstract.
In regards to claim 45 is the similar to that of claim 21 and thus is rejected using the same reasoning found in claim 21.
Response to Arguments
Applicant's arguments filed 11 July 2025 have been fully considered but they are not persuasive. The applicant argues:
The applicant appears to attempting to overcome the rejection under 35 USC 112 lack of enablement by explaining what each element that lack enablement is, however these explanation are not enough to overcome the rejection under 35 USC 112 as the specification does support for enablement of these items. Also the amendment the specification as an attempt to overcome the rejection is also improper as the amendment the specification is new matter as there was no support for the any of the amendment in the original filed specification. The applicant further argues that cited prior art reference Rojas-Cordova et al. is not an analogous prior art reference and point the written opinion of the international searching authority postmarked Feb. 16, 2023 for support. Applicant cites that written opinion of the of the international searching authority finds the prior art reference Rojas-Cordova to teaches some concepts and/or aspects of the claims limitations but does not teach the claim limitation in their entirety. The examiner respectfully traverses the applicant arguments as the examiner is not bound to the written opinion of the international searching authority and must perform a search and written opinion using the broadest reasonable interpretation and the rules and disclose in MPEP. Additionally, the examiner does not rely solely on the Rojas-Cordova et al. reference but instead does a rejection under 35 USC 103 using the prior art references Johnson (“Disruptive Innovation and Digital Transformation: 21st Century New Growth Engines”) in view of Rojas-Cordova et al. – (“Predicting Business Innovation Intention Based on Perceived Barriers: A Machine Learning Approach” – hereinafter Rojas-Cordova) in view of Yang et al. (“Deep Neural Decision Trees” – hereinafter Yang). Additional examiner maintains the combination of the Johnson in view of Rojas reference would be obvious as both reference deal with predicting business innovations, and the benefit of doing so it creates a system that does it faster and more efficiently. As such the examiner maintains the rejection.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PAULINHO E SMITH whose telephone number is (571)270-1358. The examiner can normally be reached Mon-Fri. 10AM-6PM CST.
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/PAULINHO E SMITH/Primary Examiner, Art Unit 2127