**4.4 The processor element**

This module will include the AI computation power, mainly:


6.InnovationSP map processing

### See **Figure 9**.

**Challenge Based Process Overview:** The human innovator will query the AIA with a particular innovation challenge (The innovation challenge of reference,

**119**

**Figure 10.**

*Map pathway dynamics.*

*Artificial Intelligence Assisted Innovation DOI: http://dx.doi.org/10.5772/intechopen.96112*

ICR). The AIA engine will evaluate the ICR and issue (i) a probability statement for the ICR to be solved directly within a preset time and within preset resources; (ii) resource requirements (time, budget) for identifying (B) breakdown ICs, (X) extension IC, and (A) abstraction IC. Each estimated resource requirement will be accompanied by a credibility metrics of that estimate. Each of the resultant 'switched IC' (ICS) will be estimated per its chance to be resolved directly, and the chance to resolve the ICR given the ICS was resolved, will also be estimated, along with a credibility assignment. This AIA report will be evaluated by the innovator

1.**Nominal Situation:** the option with the highest estimate credibility will be

2.**Stressed Situation:** the option estimated for minimum requirement of re-

Following his or her decision, the innovator will do one of the following:

If option 1 is successful, the process terminates. If option 2 is successful, the innovator backs off to the ICR. The dynamics is depicted in **Figure 10**: (map

The figure shows the ICR on the left, branching out to a set of switch ICs as the AIA indicates. One of the pointed ICs, is selected for another AIA round, identifying a second set of switched (2nd generation) ICs, and so on until the switched IC can be

The figures depict a path forward on the Innovation Map leading from the ICR to the last IC (ICL), which is easy enough to resolve directly. This terminates the initial forward path (see the left side of the figure). On the right side the backwards path is depicted where the innovator climbs back from the ICL, tracing the forward path. Upon arriving at a particular IC (ICB), the innovator runs into difficulties. ICB cannot be solved directly (although all the ICs from ICL to ICB were properly solved). In that case the innovator branches off from ICB, and starts another

directly solved. Once so, the process backs up, all the way to the ICR.

When drawn on the map, the process may look as follows (**Figure 11**).

3.**Exploratory Innovation:** the option with the lowest credibility assessment will be selected, since it is expected to present more productive innovation load.

who will decide on the next step based on the following conditions:

selected, regardless of its resource consumption.

sources (time, budget) will be selected.

2.Address the most promising ICS directly.

3.Re-Query the AIA over the most promising ICS.

1.Address the ICR directly.

pathway dynamics).

**Figure 9.** *AIA processor.*

*Artificial Intelligence Assisted Innovation DOI: http://dx.doi.org/10.5772/intechopen.96112*

*Artificial Intelligence - Latest Advances, New Paradigms and Novel Applications*

mode emulating human to human innovation discussion.

alarms, and do so in a perfectly conversational manner.

The conversation with the human innovator (HI) will range from pre-defined forms in which the HI just enters specific parameters, up to full free conversational

**Emulating Human to Human Innovation Discussion:** For this module one applies the current technology for human conversation emulation, complete with colorful words and expressions familiar to the HI. The conversation will be replete with "Hmm....", "I am not sure", and "How about that?" etc. The add-on of the AIA is to deploy randomness options; accordingly the AIA will suggest randomized

The AIA may be programmed to participate in a conversation where more than

**Challenge Based Process Overview:** The human innovator will query the AIA with a particular innovation challenge (The innovation challenge of reference,

An important part of this conversational module is to imprint warnings and

one HI speak together, and even to accommodate other AIA modules.

This module will include the AI computation power, mainly:

**4.3 The dialog module**

ideas, emulating 'brain storming'.

**4.4 The processor element**

2.Monte Carlo State Evaluation

6.InnovationSP map processing

1.The AI engine

3.BiPSA operation

5.The AI database

See **Figure 9**.

4.multi-variate analysis

**118**

**Figure 9.** *AIA processor.* ICR). The AIA engine will evaluate the ICR and issue (i) a probability statement for the ICR to be solved directly within a preset time and within preset resources; (ii) resource requirements (time, budget) for identifying (B) breakdown ICs, (X) extension IC, and (A) abstraction IC. Each estimated resource requirement will be accompanied by a credibility metrics of that estimate. Each of the resultant 'switched IC' (ICS) will be estimated per its chance to be resolved directly, and the chance to resolve the ICR given the ICS was resolved, will also be estimated, along with a credibility assignment. This AIA report will be evaluated by the innovator who will decide on the next step based on the following conditions:


Following his or her decision, the innovator will do one of the following:


If option 1 is successful, the process terminates. If option 2 is successful, the innovator backs off to the ICR. The dynamics is depicted in **Figure 10**: (map pathway dynamics).

The figure shows the ICR on the left, branching out to a set of switch ICs as the AIA indicates. One of the pointed ICs, is selected for another AIA round, identifying a second set of switched (2nd generation) ICs, and so on until the switched IC can be directly solved. Once so, the process backs up, all the way to the ICR.

When drawn on the map, the process may look as follows (**Figure 11**).

The figures depict a path forward on the Innovation Map leading from the ICR to the last IC (ICL), which is easy enough to resolve directly. This terminates the initial forward path (see the left side of the figure). On the right side the backwards path is depicted where the innovator climbs back from the ICL, tracing the forward path. Upon arriving at a particular IC (ICB), the innovator runs into difficulties. ICB cannot be solved directly (although all the ICs from ICL to ICB were properly solved). In that case the innovator branches off from ICB, and starts another

**Figure 10.** *Map pathway dynamics.*

#### **Figure 11.**

*Innovation Turing machine forward and backwards path.*

forward path. As depicted the new forward path progresses to a second last IC: ICL2. From there it regresses to ICR where the process terminates.

**Monte Carlo State Evaluation:** The Monte Carlo Procedure is the central activity of the AIA processor. It is used to estimate cost-to-complete and time-tofinish, and its credibility. Its results are used to judge 'next steps' options.

In describing the Monte Carlo method for this purpose we adopt the integral sign over the cost probability function. We regard f(x) as the cost probability function of cost-bearing entity x, where the chance for x to cost between a low level, L, and a high level, H is given by:

$$C(L \not\le X \not\le H) = \int\_{L}^{H} f(X) \, dx \tag{3}$$

Thereby f reflects both the estimated cost and the credibility of the estimate. We also 'steal' the integral sign to denote Monte Carlo integration of two or more probability functions:

$$\mathbf{f}\{\mathbf{x}+\mathbf{y}\} = \int\_{0}^{\bullet} \mathrm{MC}\{f\{\mathbf{x}\} \star f\{\mathbf{y}\}\}d\boldsymbol{\varepsilon} \tag{4}$$

where the integration in special cases can be taken over different boundaries across the cost parameter, c. In this Monte Carlo context the limit of the integral sign will reflect the interval of values from where a randomized selection is made. Given a query regarding the ICR = "o", the AIA will respond with a cost probability function f'(o). If f'(o) is too high for the innovator, then the innovator will prompt the AIA to estimate the cost probability functions for the three derived IC: *O* → *x*, *O* → *b*, *O*→*a*: *f(x), f(b), f(a),* and the corresponding cost probability functions for the resolution of o, given the respective derived IC was resolved: f(o|x), f(o|b), f(o|a). x, b, o designates extension, breakdown, abstraction respectively. The derived estimate for o will then be computed based on f(o||x), f(o||b) and f(o||a) where:

$$\int f\left(o\mid\vert e\right\vert\right) = \int \text{MC}\left(f\left(e\right) + f\left(o\vert e\right)\right) dc\tag{5}$$

**121**

with". **Figure 12**: "BiPSA Network".

*Artificial Intelligence Assisted Innovation DOI: http://dx.doi.org/10.5772/intechopen.96112*

estimation in [13–16].

answer surfaces.

where e stands for x, a, or b, and the derived cost for o will be:

*f o MC f o x f o b f o a dc* ( || ( || ) ( || ))

The innovator will choose among the x,b,a options according to the innovation mode, as described above (nominal, stressed, exploratory). More on R&D cost

**BiPSA Operation:** BiPSA (Binary Polling Scenario Analysis) is a discriminant analysis tool engineering to accommodate large number of discrimination factors. The BiPSA processor is working both for human respondents and for functional respondents. It is responsible for "Bipsizing" all questions to a series of binary options, the results for which progress into the answer for the original question. This is readily proven through Gödel numbers. A solution to any complex problem can be expressed via some set of elements and their relationships. Gödel has shown that such set can be represented arithmetically by an integer, G. One can then construct a series of binary questions: is G smaller or larger than some arbitrary value R. The answer limits the interval for G. The next question will ask whether G is located in the lower half of that interval or the higher half thereto. And the resultant half can again be halved again with a binary question, until G is pin pointed, and the answer to the complex question is given. Based on this premise AIA will use a BiPSA network to answer complex innovation questions. Because each question is binary, it will be readily possible to integrate the various answers coming from different knowledge sources in the system. Questions regarding path choices, cost to complete, time to finish, etc., are readily answered through progressive binary questions where each question is answered by any element of relevant knowledge in the system. As the binary questions progress the detailed

The BiPSA processor includes the randomized sub processor to devise new innovation scenarios to be BiPSA processed. BiPSA is an adaptive network comprised of BiPSA elements. A BiPSA element is an operation that accepts n inputs, x1, x2, .... xn, all in a form of integers in the range {-N:+N}, where N is an arbitrary integer. The BiPSA element responds to such input tuple with a single integer y in the same range: −*N* ≤ *y ≤ +N*. The BiPSA operator is symmetrical for change of signs. Its prime output is the sign of y. Its secondary output is the absolute value of y, which is regarded as the confidence measure of the prime answer. The

BiPSA output can be threaded as an input to a next BiPSA element, and thereby a given set of BiPSA inputs may be processed through a network of BiPSA elements. The BiPSA network is adaptive, and can be constructed as any adaptive algorithm. It reflects the growing wisdom of the system based on its expanding innovation database. **Figure 12** "BiPSA Network" depicts a network comprised of 6 BiPSA elements: B1, B2, B3, B4, B5, B6 processing n BiPSA inputs: x1, x2, .... xn into a BiPSA output, y. The network can be defined algebraically as (xij, Bk), to indicate that input xi is threaded through element Bk at position j. In **Figure 12** if x1 is the top input then we write: (x11,B1), (x12,B4), (x13,B6) and the same for all the inputs. This algebraic definition can be adapted to best reflect the accumulated knowledge. Also note that for any value of n, there can be infinite number of BiPSA elements to "play

BiPSA lends itself to genetic adaptation with increasing network complexity. One common expansion for BiPSA is to break the input tuple to confidence levels, to be subsequently integrated, as illustrated in **Figure 13**: "BiPSA Confidence Mapping":

prime answer is binary (positive or negative), or 'no answer' (y = 0).

= ++ ò (6)

( ) ( ) 0

¥

*Artificial Intelligence - Latest Advances, New Paradigms and Novel Applications*

forward path. As depicted the new forward path progresses to a second last IC:

**Monte Carlo State Evaluation:** The Monte Carlo Procedure is the central activity of the AIA processor. It is used to estimate cost-to-complete and time-to-

In describing the Monte Carlo method for this purpose we adopt the integral sign over the cost probability function. We regard f(x) as the cost probability function of cost-bearing entity x, where the chance for x to cost between a low level,

£ £ ò ( )= ( ) *<sup>H</sup>*

Thereby f reflects both the estimated cost and the credibility of the estimate. We also 'steal' the integral sign to denote Monte Carlo integration of two or more

( ) ( ( ) ( )) ¥

where the integration in special cases can be taken over different boundaries across the cost parameter, c. In this Monte Carlo context the limit of the integral sign will reflect the interval of values from where a randomized selection is made. Given a query regarding the ICR = "o", the AIA will respond with a cost probability function f'(o). If f'(o) is too high for the innovator, then the innovator will prompt the AIA to estimate the cost probability functions for the three derived IC: *O* → *x*, *O* → *b*, *O*→*a*: *f(x), f(b), f(a),* and the corresponding cost probability functions for the resolution of o, given the respective derived IC was resolved: f(o|x), f(o|b), f(o|a). x, b, o designates extension, breakdown, abstraction respectively. The derived estimate for o will then be computed based on f(o||x), f(o||b) and

( ) ( ( ) ( ))

*f o e MC f e f o e dc* |

0

¥

*<sup>L</sup> C L X H f X dx* (3)

ò<sup>0</sup> f += *x y MC f x + f y dc* (4)

= + ò (5)

finish, and its credibility. Its results are used to judge 'next steps' options.

ICL2. From there it regresses to ICR where the process terminates.

L, and a high level, H is given by:

*Innovation Turing machine forward and backwards path.*

probability functions:

**Figure 11.**

**120**

f(o||a) where:

where e stands for x, a, or b, and the derived cost for o will be:

$$f\left(o\right) = \bigcup\_{o}^{\circ} \mathrm{MC}\left(f\left(o||\chi\right) + f\left(o||b\right) + f\left(o||a\right)\right)dc\tag{6}$$

The innovator will choose among the x,b,a options according to the innovation mode, as described above (nominal, stressed, exploratory). More on R&D cost estimation in [13–16].

**BiPSA Operation:** BiPSA (Binary Polling Scenario Analysis) is a discriminant analysis tool engineering to accommodate large number of discrimination factors. The BiPSA processor is working both for human respondents and for functional respondents. It is responsible for "Bipsizing" all questions to a series of binary options, the results for which progress into the answer for the original question. This is readily proven through Gödel numbers. A solution to any complex problem can be expressed via some set of elements and their relationships. Gödel has shown that such set can be represented arithmetically by an integer, G. One can then construct a series of binary questions: is G smaller or larger than some arbitrary value R. The answer limits the interval for G. The next question will ask whether G is located in the lower half of that interval or the higher half thereto. And the resultant half can again be halved again with a binary question, until G is pin pointed, and the answer to the complex question is given. Based on this premise AIA will use a BiPSA network to answer complex innovation questions. Because each question is binary, it will be readily possible to integrate the various answers coming from different knowledge sources in the system. Questions regarding path choices, cost to complete, time to finish, etc., are readily answered through progressive binary questions where each question is answered by any element of relevant knowledge in the system. As the binary questions progress the detailed answer surfaces.

The BiPSA processor includes the randomized sub processor to devise new innovation scenarios to be BiPSA processed. BiPSA is an adaptive network comprised of BiPSA elements. A BiPSA element is an operation that accepts n inputs, x1, x2, .... xn, all in a form of integers in the range {-N:+N}, where N is an arbitrary integer. The BiPSA element responds to such input tuple with a single integer y in the same range: −*N* ≤ *y ≤ +N*. The BiPSA operator is symmetrical for change of signs. Its prime output is the sign of y. Its secondary output is the absolute value of y, which is regarded as the confidence measure of the prime answer. The prime answer is binary (positive or negative), or 'no answer' (y = 0).

BiPSA output can be threaded as an input to a next BiPSA element, and thereby a given set of BiPSA inputs may be processed through a network of BiPSA elements.

The BiPSA network is adaptive, and can be constructed as any adaptive algorithm. It reflects the growing wisdom of the system based on its expanding innovation database. **Figure 12** "BiPSA Network" depicts a network comprised of 6 BiPSA elements: B1, B2, B3, B4, B5, B6 processing n BiPSA inputs: x1, x2, .... xn into a BiPSA output, y. The network can be defined algebraically as (xij, Bk), to indicate that input xi is threaded through element Bk at position j. In **Figure 12** if x1 is the top input then we write: (x11,B1), (x12,B4), (x13,B6) and the same for all the inputs. This algebraic definition can be adapted to best reflect the accumulated knowledge. Also note that for any value of n, there can be infinite number of BiPSA elements to "play with". **Figure 12**: "BiPSA Network".

BiPSA lends itself to genetic adaptation with increasing network complexity. One common expansion for BiPSA is to break the input tuple to confidence levels, to be subsequently integrated, as illustrated in **Figure 13**: "BiPSA Confidence Mapping":

**Figure 12.** *BiPSA network.*

**Figure 13.** *BiPSA confidence mapping.*

The input tuple {+3, −2, −2, +1} is processed as is in the upper element with an output of +1. A second element is processing only input values of confidence level 2 or above, resulting with an output of +0, and the third BiPSA element is processing only input values of confidence level 3. All the three respective output values are further BiPSA integrated to an outcome of +2.

Note: the BiPSA methodology is documented in various references [17].

**Multivariate Analysis:** Innovation is an ever growing practice, millions of innovation effort hours are being recorded worldwide. Every innovation effort can be mapped onto a pathway on the innovation map. Innovation challenges may be characterized by a very large number of parameters. This ever-increasing

**123**

in [18].

**4.5 The environment element E**

1.Collaborating Teams

2.Proprietary Sources

3.Public Sources

*Artificial Intelligence Assisted Innovation DOI: http://dx.doi.org/10.5772/intechopen.96112*

database is the raw material for a potent multivariate analysis (or rather magavariate analysis). This worldwide database is not openly shared, and thus large organizations that can assemble the entire innovation history into a database will have a distinct advantage. However, there is a considerable amount available on the public domain. Technical papers may be used to map a solution on the innovation map. Every published patent can be matched with an innovation challenge it solves. One may envision future service companies that would map public domain and some private innovation pathways onto a standard data platform (the innovation map). This database will serve as a guide for any current innovation challenge. This approach is inspired by the surprising success achieved by language translators that rely not on complex man-elucidated rules of linguistics, but on an ever larger database of properly translated text between the two languages of reference. The very large number of analyzed parameters suggest the BiPSA methodology as an effective multivariate analysis tool. We consider some n arguments x1, x2, .... xn

which are used as input to determine the value of an output variable y.

Typically f is not known. What is given is some k data points where in case i = 1,2…k the values of the arguments is x1i, x2i,..... xni and the respective output is known as yi:

The function f can be approximated through one of the many techniques developed for the purpose. However, most of the common solutions are based on high dimensional metric spaces and complex cluster analysis. These are methods

The environment element will connect to the environment comprised of:

**Interval Based Operation:** let the k y values be organized by increasing order, so that yi ≤ y i + 1 for i = 1,2,…(k-1). Let y0 be the median, or the average of the k y values, or sufficiently close to it, and let y0 ≠ yi for i = 1,2,…,k then the k data points are divided between those pointing to y values above the median and data points pointing to y values below the median. Given any new set of n input parameters, not listed in the k points database, then the binary question would be, is the corresponding y value above or below the median. Once answered the roughly k/2 points in the section of the y range pointed to by the first BiPSA question, will be used for in the same way to further pinpoint y in the upper or lower half of the established y range, and so on, pinpointing y as needed. The credibility of the answers will decrease as each successive BiPSA question uses about half of the data points used before. The larger y intervals will be stated with greater confidence. Fuzzy logic neural network tools are a good alternative to BiPSA, as discussed

that get prohibitive when the value of n increases. Not so with BiPSA.

y f x ,x ,.. x = ¼ ( 12 n ) (7)

y f x ,x ,.. x i 1i 2i = ¼ ( *ni*) (8)

#### *Artificial Intelligence Assisted Innovation DOI: http://dx.doi.org/10.5772/intechopen.96112*

*Artificial Intelligence - Latest Advances, New Paradigms and Novel Applications*

The input tuple {+3, −2, −2, +1} is processed as is in the upper element with an output of +1. A second element is processing only input values of confidence level 2 or above, resulting with an output of +0, and the third BiPSA element is processing only input values of confidence level 3. All the three respective output values are

Note: the BiPSA methodology is documented in various references [17]. **Multivariate Analysis:** Innovation is an ever growing practice, millions of innovation effort hours are being recorded worldwide. Every innovation effort can be mapped onto a pathway on the innovation map. Innovation challenges may be characterized by a very large number of parameters. This ever-increasing

further BiPSA integrated to an outcome of +2.

**122**

**Figure 13.**

**Figure 12.** *BiPSA network.*

*BiPSA confidence mapping.*

database is the raw material for a potent multivariate analysis (or rather magavariate analysis). This worldwide database is not openly shared, and thus large organizations that can assemble the entire innovation history into a database will have a distinct advantage. However, there is a considerable amount available on the public domain. Technical papers may be used to map a solution on the innovation map. Every published patent can be matched with an innovation challenge it solves. One may envision future service companies that would map public domain and some private innovation pathways onto a standard data platform (the innovation map). This database will serve as a guide for any current innovation challenge. This approach is inspired by the surprising success achieved by language translators that rely not on complex man-elucidated rules of linguistics, but on an ever larger database of properly translated text between the two languages of reference. The very large number of analyzed parameters suggest the BiPSA methodology as an effective multivariate analysis tool. We consider some n arguments x1, x2, .... xn which are used as input to determine the value of an output variable y.

$$\mathbf{y} = \mathbf{f}\left(\mathbf{x}\_1, \mathbf{x}\_2, \dots, \mathbf{x}\_n\right) \tag{7}$$

Typically f is not known. What is given is some k data points where in case i = 1,2…k the values of the arguments is x1i, x2i,..... xni and the respective output is known as yi:

$$\mathbf{y}\_{i} = \mathbf{f}\left(\mathbf{x}\_{1i}, \mathbf{x}\_{2i}, \dots, \mathbf{x}\_{nl}\right) \tag{8}$$

The function f can be approximated through one of the many techniques developed for the purpose. However, most of the common solutions are based on high dimensional metric spaces and complex cluster analysis. These are methods that get prohibitive when the value of n increases. Not so with BiPSA.

**Interval Based Operation:** let the k y values be organized by increasing order, so that yi ≤ y i + 1 for i = 1,2,…(k-1). Let y0 be the median, or the average of the k y values, or sufficiently close to it, and let y0 ≠ yi for i = 1,2,…,k then the k data points are divided between those pointing to y values above the median and data points pointing to y values below the median. Given any new set of n input parameters, not listed in the k points database, then the binary question would be, is the corresponding y value above or below the median. Once answered the roughly k/2 points in the section of the y range pointed to by the first BiPSA question, will be used for in the same way to further pinpoint y in the upper or lower half of the established y range, and so on, pinpointing y as needed. The credibility of the answers will decrease as each successive BiPSA question uses about half of the data points used before. The larger y intervals will be stated with greater confidence.

Fuzzy logic neural network tools are a good alternative to BiPSA, as discussed in [18].

#### **4.5 The environment element E**

The environment element will connect to the environment comprised of:


**Collaborating Teams:** The given AIA will benefit from exchange with AIA machines operated by collaborating teams. The various AIA will agree on a sharing protocol that may be based on free updating of a shared access database, or on a push or pull configuration. Issues of cyber security will play a role in the selection of the right sharing solution.

A sophisticated AIA will use Homomorphic encryption to handle the division of confidentiality within the team members. This will account for collaborating teams who keep some proprietary information confidential while sharing the rest of the material.

**Proprietary Sources:** Companies like R. S. Means are selling innovation related data for a price. Mostly they are subscription based, but some international purveyors offer pay-as-you-go, requiring the AIA to use digital money to pay per services rendered real time.

**Public Sources:** The share of global information freely available to the public is growing exponentially. The efficiency of the major search engines like Google and Bing is improving daily. Yet, the utility of these search engines depends largely on the selected search keywords. It is the responsibility of the AIA software to translate the need for information into proper key words string.

A critical source of innovation related information is government. By law most democratic governments publish a wealth of data regarding public projects. This data is very useful for an effective AIA. One important government source is the patent office which publishes new patents in very searchable forms.

## **5. Outlook**

Thomas Edison, Bill Gates, Alexander Graham Bell are examples of top tier innovators who changed our lives in a fundamental way. However societal progress is taking place via a myriad of non-famous innovators, each making a small innovative step forward. These run of the mill innovators are the target beneficiaries of AIAI. The Steve Jobs and the James Watts among us do not need the help offered by AIAI, but most of us are better served by advanced guidance to channel our creativity into a productive pathway.

We see AIAI making great progress in the dialog part with the innovator, and the other innovation stakeholders, in the interaction with innovation contributors from the outside.

The profound contribution of AIAI to the innovation process is in (i) a comprehensive exploitation of rich innovation history, and (ii) in advanced Monte Carlo computation of credible estimates of cost to complete and time to finish the innovation process. We witness a world with a "global library" as exemplified via Google, Bing, Yahoo, and Baidu and proprietary systems like R. S. Means offer a rich "digging ground" for sophisticated AIAI systems. Also, innovation is an ever-growing enterprise and invariably there are more research ideas than there are resources for them all. A competition ensues. The AIAI methodology centered around credible cost estimates leads to rational allocation of these scarce resources, all for the benefit of society at large.

The solution to most of the pressing and universal problems of humanity is to be found in the promise of innovation, and hence a tool to make the innovation effort more productive, is a welcome addition to the tool-box we use to meet our future.

#### **Acknowledgments**

This work owes its existence to my old teacher, Professor Ephraim Kehat, who encouraged me to pause my full-steam engineering practice, and dig deep into

**125**

**Author details**

Case Western Reserve University, Cleveland, OH, USA

\*Address all correspondence to: gideon.samid@case.edu

provided the original work is properly cited.

© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

Gideon Samid

*Artificial Intelligence Assisted Innovation DOI: http://dx.doi.org/10.5772/intechopen.96112*

generic engineering principles of innovation, which became my PhD dissertation, and was subsequently boosted with artificial intelligence tools to mature into "Artificial Intelligence Assisted Innovation" (AIAI), summarized herein. My father Ya'acov, and my brother Amnon, both engineers, supported this daring track, and

my beloved partner, Dolores, has kept me going, and still does.

*Artificial Intelligence Assisted Innovation DOI: http://dx.doi.org/10.5772/intechopen.96112*

*Artificial Intelligence - Latest Advances, New Paradigms and Novel Applications*

of the right sharing solution.

services rendered real time.

**5. Outlook**

the outside.

the need for information into proper key words string.

channel our creativity into a productive pathway.

patent office which publishes new patents in very searchable forms.

Thomas Edison, Bill Gates, Alexander Graham Bell are examples of top tier innovators who changed our lives in a fundamental way. However societal progress is taking place via a myriad of non-famous innovators, each making a small innovative step forward. These run of the mill innovators are the target beneficiaries of AIAI. The Steve Jobs and the James Watts among us do not need the help offered by AIAI, but most of us are better served by advanced guidance to

We see AIAI making great progress in the dialog part with the innovator, and the other innovation stakeholders, in the interaction with innovation contributors from

The profound contribution of AIAI to the innovation process is in (i) a comprehensive exploitation of rich innovation history, and (ii) in advanced Monte Carlo computation of credible estimates of cost to complete and time to finish the innovation process. We witness a world with a "global library" as exemplified via Google, Bing, Yahoo, and Baidu and proprietary systems like R. S. Means offer a rich "digging ground" for sophisticated AIAI systems. Also, innovation is an ever-growing enterprise and invariably there are more research ideas than there are resources for them all. A competition ensues. The AIAI methodology centered around credible cost estimates leads to rational

The solution to most of the pressing and universal problems of humanity is to be found in the promise of innovation, and hence a tool to make the innovation effort more productive, is a welcome addition to the tool-box we use to meet our future.

This work owes its existence to my old teacher, Professor Ephraim Kehat, who encouraged me to pause my full-steam engineering practice, and dig deep into

allocation of these scarce resources, all for the benefit of society at large.

**Collaborating Teams:** The given AIA will benefit from exchange with AIA machines operated by collaborating teams. The various AIA will agree on a sharing protocol that may be based on free updating of a shared access database, or on a push or pull configuration. Issues of cyber security will play a role in the selection

A sophisticated AIA will use Homomorphic encryption to handle the division of confidentiality within the team members. This will account for collaborating teams who keep some proprietary information confidential while sharing the rest of the material. **Proprietary Sources:** Companies like R. S. Means are selling innovation related data for a price. Mostly they are subscription based, but some international purveyors offer pay-as-you-go, requiring the AIA to use digital money to pay per

**Public Sources:** The share of global information freely available to the public is growing exponentially. The efficiency of the major search engines like Google and Bing is improving daily. Yet, the utility of these search engines depends largely on the selected search keywords. It is the responsibility of the AIA software to translate

A critical source of innovation related information is government. By law most democratic governments publish a wealth of data regarding public projects. This data is very useful for an effective AIA. One important government source is the

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**Acknowledgments**

generic engineering principles of innovation, which became my PhD dissertation, and was subsequently boosted with artificial intelligence tools to mature into "Artificial Intelligence Assisted Innovation" (AIAI), summarized herein. My father Ya'acov, and my brother Amnon, both engineers, supported this daring track, and my beloved partner, Dolores, has kept me going, and still does.
